Reconstituting transcription–translation-coupled DNA replication within complex in vitro biological systems

Domain: biology
Published: 2025-12-15
Editorial outcome: major_revision

Abstract
Reconstructing transcription–translation-coupled DNA replication (TTcDR) in artificial systems is crucial for creating synthetic life; however, existing approaches face limitations mainly due to their reliance on purified biological components. Here, we introduce LoopReX, a cell-free system that reconstitutes TTcDR using crude Escherichia coli extracts, offering a more complex native biological environment. LoopReX leverages a minimal machinery composed of phi29 DNA polymerase and T7 RNA polymerase, with the latter facilitating DNA replication initiation through the generation of primer RNAs. Using machine learning, we optimize LoopReX to enhance the efficiency of both DNA replication and protein expression, achieving scalable, sustainable genetic flow and high-yield protein production with robust iterative performance. Furthermore, artificial nucleoids, autonomously formed through CipB-based compartmentalization, improve DNA spatial organization and support multiple biological functions. This work advances the construction of artificial life by reconstituting TTcDR within a single, scalable, and functionalized system, opening exciting possibilities for synthetic biology, biotechnology, and bio-hybrid applications.

Introduction
The construction of artificial life aims to design and synthesize “living” systems capable of exhibiting fundamental characteristics such as replication, metabolism, compartmentalization, and communication1–4. At the core of these processes, the central dogma of molecular biology—the flow of genetic information from DNA to RNA to proteins—serves as a foundational framework. Reconstructing transcription–translation-coupled DNA replication (TTcDR) in vitro is an essential step toward the creation of artificial life systems5. The PURE (Protein synthesis Using Recombinant Elements)-based cell-free expression system, which functions in a well-defined (bio)chemical environment6, has been widely applied to reconstitute specific life-like processes such as DNA replication7–12, gene sequence evolution13–15, cell division-like events16–18, artificial–natural cell communication19–23, and light-driven ATP regeneration24. However, these efforts typically address isolated functions, and integrating multiple life characteristics into a single artificial system remains a major challenge given the relatively simplified nature of the PURE platform.

Recently, researchers have explored the use of cell lysates to construct artificial cells that more closely resemble the cytoplasm of living cells25,26. Cell lysates, which contain a broad array of native biomolecules and biocatalysts, provide a more complex and dynamic environment for reconstituting life-like processes. For example, autonomous ribosome biogenesis has been achieved in vitro using Escherichia coli cell extracts, marking a crucial step toward artificial cell development25. Additionally, the use of two bacterial lysates has enabled the creation of synthetic cells capable of transcription, translation, and some life-like features, such as proto-nuclei and cytoskeletal structures26. Despite these advances, to our knowledge, TTcDR has not yet been reconstituted within complex, lysate-based in vitro biological systems.

Here, we present the LoopReX (Loop of DNA replication and protein expression) system, an E. coli crude extract-based cell-free gene expression (CFE) platform designed to reconstitute TTcDR in vitro. Specifically, LoopReX integrates four key modules: (1) DNA replication, driven by phi29 bacteriophage DNA polymerase (phi29DNAP), (2) transcription, mediated by T7 bacteriophage RNA polymerase (T7RNAP), (3) translation, supported by ribosomes and translation-related factors within the cell extract, and (4) an artificial nucleoid, assembled using a bacterial CipB scaffold to spatially compartmentalize DNA for efficient TTcDR processes. In contrast to previous reconstituted systems such as PURE7–9, where replication machinery is encoded and expressed in situ, our approach uses a cell extract-based system supplemented with purified DNA polymerase. This enables robust, immediate replication and provides a versatile platform for tuning replication independently of protein synthesis, while leveraging the complex biochemical environment of the lysate. We initially demonstrate that LoopReX effectively supports DNA replication and elucidate the synergetic mechanisms between phi29DNAP and T7RNAP. Additionally, through optimization via machine learning strategies, we achieve a robust and scalable system that enables sustainable genetic iteration with the production of both DNA and protein. Furthermore, we show that the artificial nucleoid not only enhances the performance of in vitro TTcDR but can also be used to integrate functional monomers, such as enzymes, to reconstitute artificial metabolic networks. By successfully reconstituting TTcDR within a complex crude extract-based cell-free expression system, the LoopReX platform represents a significant advancement in the construction of artificial life. We anticipate that this system will provide valuable insights and opportunities for the design, construction, and study of artificial cells in the future.

Results
To establish the TTcDR process in vitro, we selected the E. coli crude lysate-based CFE system, which has been extensively developed and widely used for various applications27–36, and phi29DNAP as the DNA replication machinery due to its exceptionally high processivity (up to 70 kb per single synthesis event) and its superior DNA synthesis fidelity, conferred by its 3’→5’ exonuclease proofreading activity37,38. We first assessed the activity of phi29DNAP in a pure chemical reaction buffer using circular single-stranded DNA (cssDNA, ssM13) as a template. The results demonstrated that phi29DNAP exhibited robust DNA polymerase activity in the reaction buffer (Supplementary Figs. 1 and 2). However, DNA replication efficiency declined over time (Supplementary Fig. 3), likely due to phi29DNAP’s exonuclease activity, depletion of replication initiation complexes (circular single-stranded DNA + initiation primer), and accumulation of inhibitory byproducts such as pyrophosphate (Supplementary Fig. 4)7,12,37,39.

Following the initial experiments, we introduced the DNA replication machinery into the crude extract-based CFE system, which we term LoopReX. It is important to note that the DNA replication in our system proceeds via phi29DNAP-based rolling circle amplification and is therefore non-recursive—the replicated DNA does not recapitulate the original circular template. This differs from canonical chromosomal replication in vivo, but still provides a powerful and programmable means of DNA amplification and template regeneration in vitro7–12. To begin, we used cssDNA (ssM13) as the template and confirmed that phi29DNAP is active in the CFE system (Fig. 1a). We then replaced the cssDNA template with circular double-stranded DNA (cdsDNA, pJL1-T7-sfGFP) and also observed successful replication of pJL1-T7-sfGFP in LoopReX (Fig. 1b). However, the replication efficiency of phi29DNAP was noticeably lower for cdsDNA compared to cssDNA (Fig. 1a, b). This suggests that cdsDNA requires strand separation and synthesis of replication initiation primers (which were not added to the reaction) before phi29DNAP can initiate the DNA replication process (Fig. 1b). In contrast, these prerequisites already exist in the cssDNA reactions from the outset (Fig. 1a). This modest (1.3-fold, Fig. 1b) but reproducible increase in DNA quantity indicates that the LoopReX system supports limited DNA replication of circular plasmids under current conditions, establishing a foundation for further optimization.

Next, we selected pJL1-T7-sfGFP as the template in our LoopReX system because cdsDNA mimics the natural double-stranded genome and remains stable in the extract-based CFE system (Fig. 1b, see the “CFE system” group). Using this, we simultaneously analyzed DNA replication and protein expression in LoopReX over a 6 h period (Fig. 1c, d). We observed that DNA replication and protein expression occurred on different temporal scales. Specifically, DNA content remained high during the first hour and then gradually decreased over the next 5 h (Fig. 1c), likely due to nuclease-mediated degradation in the cell lysate. Nonetheless, LoopReX consistently maintained higher DNA levels than the control CFE system at all time points. Despite the reduction in DNA, protein expression steadily increased over 5 h before plateauing (Fig. 1d). Additionally, protein expression in LoopReX reached its maximum earlier than in the control CFE system, suggesting a faster expression rate that offers advantages in efficiency and throughput. Taken together, these findings demonstrate that LoopReX outperforms the CFE system in both DNA replication and protein expression, providing a robust platform for subsequent optimization.

Having reconstructed TTcDR in vitro, we next sought to elucidate the mechanism of DNA replication initiation, which remains unclear. Previous studies have suggested that RNAs transcribed by T7RNAP could serve as primers for DNA replication, as observed in the PURE system and with phi29DNAP7. To determine if this phenomenon occurs in our LoopReX system, we first tested the efficiency of DNA replication with varying amounts of T7RNAP in the reactions. We found that replication efficiency was positively correlated with the amount of T7RNAP in LoopReX (Fig. 1e, Bars 1 to 4). However, higher amounts of T7RNAP slightly inhibited DNA replication efficiency (Fig. 1e, Bars 4 to 6). These results suggest that DNA replication efficiency is directly influenced by the amount of T7RNAP in vitro.

To further investigate the mechanism of DNA replication initiation by T7RNAP, we constructed different plasmids by replacing the original T7 promoter (φ10, class III) used in pJL1-T7-sfGFP with the class II (φ1.1B and φ1.3) and class III (φ13) promoters. Previous studies have shown that these T7-based promoters performed differently during transcription, producing two types of RNA products: abortive transcription products (short, non-functional RNA chains) and full-length mRNA (used for protein synthesis)40 (Fig. 1f). While these four promoters prefer to generate different RNA products, we found that DNA replication efficiency followed the order of the total RNA product yields (defined as the sum of abortive transcription products and full-length mRNA) (Fig. 1f). Based on this, we propose that T7RNAP and its promoters facilitate the DNA replication initiation process by generating RNA products that serve as primers (note that full-length mRNA may also be degraded into short RNAs by nucleases) for DNA replication (Fig. 1g). The use of abortive transcription products as replication primers has been observed in natural DNA replication initiation mechanisms41,42 (Fig. 1g, blue route). For instance, T7DNAP uses abortive transcription products to initiate genome replication in T7 bacteriophages41,42. However, unlike abortive transcription products, the involvement of mRNA as primer sources for DNA replication by phi29DNAP is unexpected. The essence of mRNA-based primers is that mRNA provides a nucleotide sequence that can generate diverse short RNA chains. To further validate whether long nucleotide chains can serve as primer sources, we attempted to replicate cssDNA (ssM13) in LoopReX by directly using the diverse nucleotide chains contained in cell lysates (Supplementary Fig. 5). Our findings indicate that phi29DNAP can indeed utilize long nucleotide chains as primer sources for DNA replication (Fig. 1g, green route)43,44.

Building on our investigation, we demonstrate that T7 promoter, T7RNAP, and phi29DNAP can form a minimal replication unit within the LoopReX system. To enhance the efficiency of in vitro DNA replication, we next constructed plasmids with tandemly repeated promoters, designed to serve as DNA replication initiation sites (Fig. 1h). These included 2*φ10, 3*φ10, and 4*φ10 promoters, all based on the strongest φ10 promoter (Fig. 1f). As a comparison, we also constructed a plasmid with the native replication origin of T7 bacteriophages, which consist of the φ1.1A and φ1.1B promoters. Our data reveal that the tandemly repeated promoters are more effective at initiating DNA replication, with the 3*φ10 promoter improving replication efficiency by threefold compared to the 1*φ10 promoter. However, we also observed a slight reduction in protein expression yields with the tandem promoters (Fig. 1h). This suggests that using promoters as replication origins introduces a competitive dynamic between DNA replication and transcription at the promoter sites. To explore further, we investigated whether the position of the promoters affects both replication and transcription. Specifically, we defined the 3*φ10 promoter as an artificial replication origin and relocated it to different positions within the plasmid (Fig. 1i). Our results showed that when the artificial origin was positioned downstream of the terminator—far from the normal 1*φ10 promoter—the DNA replication efficiency increased compared to 1*φ10-based plasmid without the insertion of the artificial origin (Fig. 1i). In contrast, the replication efficiency of the 4*φ10-based plasmid (that is similar to the artificial origin placed upstream of the 1*φ10 promoter) was significantly reduced, reaching the lowest observed values. These findings support our hypothesis that the position of the promoters (i.e., the artificial origin) plays a crucial role in regulating DNA replication and transcription. Importantly, we also observed a remarkable increase in protein expression levels with the plasmid containing the artificial origin (Fig. 1i).

Taken together, we have established our LoopReX system with three key achievements: (i) successful reconstitution of TTcDR in vitro using phi29DNAP and T7RNAP in a cell extract-based CFE system—a more complex in vitro biological system; (ii) elucidation of the synergetic mechanisms of phi29DNAP and T7RNAP in DNA replication, enabling the construction of an efficient, two enzymes-based replication unit for LoopReX; and (iii) rational design of an artificial replication origin that enhances the overall performance of the TTcDR process, benefiting not only DNA replication but also transcription and translation.

In the LoopReX system, T7RNAP plays a crucial role in initiating DNA replication and driving transcription (Fig. 1e–i). However, in vitro processes for DNA replication, transcription, and translation often require distinct and optimized conditions within the cell-free reaction environment12. Achieving effective coordination among these processes requires careful optimization of experimental parameters to maximize overall system performance. Previous studies have employed single-factor optimization methods to improve in vitro TTcDR based on the simple PURE system7,9,12. However, due to the complexity of the components in LoopReX, single-factor optimization may not be sufficient to efficiently fine-tune the system. To overcome this, we applied machine learning to optimize both DNA replication and protein expression in LoopReX, systematically exploring millions of possible conditions to identify parameters that maximize the efficiency of both processes simultaneously.

To streamline the optimization process and allow the machine learning model to focus on key variables, we used the pJL1-T7-sfGFP plasmid (i.e., pJL1-1*φ10-sfGFP) as the template. For in silico optimization, we employed XGBoost (eXtreme Gradient Boosting)45, a decision tree-based ensemble method known for strong predictive performance, scalability, and native handling of missing values and complex feature interactions. Moreover, XGBoost mitigates overfitting and improves model generalization through regularization, learning-rate shrinkage, subsampling of rows and columns, and early stopping. We constructed XGBoost models to predict DNA replication and protein expression levels by analyzing the influence of factors from the CFE standard protocol46,47, including additional variables such as dNTPs and DTT (Fig. 2a). We evaluated 16 factors (see “Factor selection and data processing” in “Methods” for details), and all experimental data were generated in our laboratory. For model development, the datasets comprised 115 experimental conditions for DNA replication and 126 experimental conditions for protein expression, each representing a distinct combination of reactants and their concentrations in the LoopReX system. Across samples, the varying features were the identity and concentration of input reactants, while all other experimental parameters were kept constant. The collected data were randomly split, with 80% used for model training and 20% reserved for independent testing. Then, five-fold cross-validation was performed only on the training set to assess and enhance the robustness of our models. Both models achieved a strong R² value of 0.99 for the training set. For the test set, the DNA replication prediction model had an R² of 0.81, while the protein expression model reached 0.88, demonstrating the robustness and accuracy of our machine learning models (Fig. 2b). The correlation heatmap revealed notable differences in the factors influencing LoopReX compared to previously reported systems7,9,12, with clear correlations observed between several factors (Supplementary Figs. 6 and 7).

To validate these predictions, we selected five distinct reaction formulations (F1–F5) and conducted LoopReX reactions. For protein expression, we measured sfGFP fluorescence at 6 h, while DNA content was quantified after 0.5 h. The experimental results showed consistent trends in both DNA replication efficiency and protein expression efficiency that aligned with the model predictions (Fig. 2c, d and Supplementary Table 1). To further analyze the impact of the five formulations on DNA replication and protein expression (transcription-translation), we examined DNA replication efficiency using ssM13 (lacking the T7 promoter) and protein expression using pJL1-T7-sfGFP (without the addition of phi29DNAP) (Fig. 2e, f). This allowed us to separately evaluate the performance of the formulations for DNA replication and transcription-translation processes. In the absence of protein expression, DNA replication efficiency from formulations F1 to F5 progressively increased (Fig. 2e), suggesting that transcription-translation machinery in LoopReX increasingly inhibits DNA replication (Fig. 2c). Similarly, in the absence of DNA replication, protein expression efficiency also increased across formulations F1 to F5 (Fig. 2f), reinforcing this observation. However, it is worth noting that the protein expression efficiency without DNA replication (Fig. 2f) was overall lower than that in LoopReX reactions with DNA replication (Fig. 2d). This difference is likely because the newly synthesized DNA in LoopReX can also serve as templates for protein expression, thereby enhancing protein yields.

After optimization, formulation F3 (see Supplementary Table 1 for concentration details) emerged as the optimal combination for both efficient DNA replication and protein expression (Fig. 2c, d and Supplementary Fig. 8). This optimized formulation, referred to as LoopReX-Opt, was further investigated for its ability to enable coupled DNA replication and protein expression using the pJL1-T7-sfGFP plasmid. Control experiments with the pre-optimized LoopReX system (LoopReX-Ori) were also performed for comparison. We observed that DNA content in the LoopReX-Opt system approximately doubled within the first 0.5 h (Fig. 2g). This increase in DNA content within 0.5 h is in agreement with the findings from the screening experiments in Fig. 2c, where formulation F3 showed similar results. Protein expression was also significantly enhanced in LoopReX-Opt, reaching maximum yield after just 3 h (Fig. 2h). In contrast, the LoopReX-Ori system and traditional CFE systems required 8 h (Fig. 2h) and 14 h (Supplementary Fig. 9), respectively, to reach comparable protein yields, despite all three systems using the same initial DNA template concentration. Although the improvements in DNA replication and protein expression were moderate (Fig. 2g, h), these results reflect the complex interplay of reaction components in LoopReX and demonstrate the utility of machine learning for optimizing such multifactorial systems. Importantly, LoopReX-Opt accelerates genetic information transfer and provides a more efficient TTcDR framework, which could be valuable for advancing artificial life research.

With the TTcDR framework established, we next evaluated the ability of LoopReX-Opt to support the flow of genetic information. Initially, a seed solution was incubated for 1 h to replicate DNA, which was then transferred into a fresh LoopReX-Opt reaction (without DNA template) at a 4-fold dilution for subsequent rounds of DNA replication (Fig. 3a). After four rounds of reactions, we observed that LoopReX-Opt could maintain stable DNA replication, whereas the traditional CFE system failed to replicate DNA without the TTcDR framework. Notably, when a plasmid with the artificial origin (Fig. 1i) was used, LoopReX-Opt reactions resulted in greater DNA replication compared to the use of the pJL1-T7-sfGFP plasmid (Fig. 3a). This demonstrates that LoopReX-Opt enables sustained DNA amplification across serial dilutions, supporting template persistence over multiple reaction cycles.

Next, we performed successive, saturated iterative reactions, in which the entire reaction mixture from the previous round was used as a seed to inoculate multiple fresh reactions with a consistent ratio of 1:3 (seed:fresh mix) for the next round (Fig. 3b). The theoretical protein accumulation was assessed by analyzing the minimal unit of each iterative reaction (see “Methods” for details). The results revealed that the LoopReX-Opt system substantially enhanced protein accumulation with each iteration, compared to traditional CFE reactions (Fig. 3b). After the fifth round (Round 5), 1 nM of initial DNA (added in the Round 0 reaction) yielded a cumulative, theoretical protein yield of approximately 760 μg in the LoopReX-Opt system, representing a 3.6-fold increase compared to traditional CFE systems (Fig. 3b). The theoretical protein accumulation plateaued in the fourth round, suggesting that DNA replication remained effective for at least four rounds, after which it gradually ceased (Fig. 3b).

We also conducted volume scale-up iterative reactions, aligning the experimental design with standard cell cultivation protocols. In this approach, small-scale seed reactions were incubated for 1 h to maintain high DNA levels, followed by inoculation into fresh reaction mixes at a 1:10 dilution (v/v). For comparison, traditional CFE systems were also used to prepare seed solutions. In the final large-scale reaction, the seed solution was added to the traditional CFE system for protein expression, allowing us to evaluate the impact of replicated DNA levels on protein synthesis. Note that the final CFE reaction was incubated for 6 h with shaking to ensure homogeneity. Our results showed that DNA was effectively replicated during the scale-up process, serving as a template for high-level protein synthesis (Fig. 3c). Specifically, protein yields per DNA input in (i) single- and (ii) double-amplification reactions (up to 1 mL) increased by 9.5- and 15-fold, respectively, compared to the standard 15 μL LoopReX-Opt reaction. This indicates that in vitro amplification not only reduces DNA input requirements but also substantially enhances overall product yield.

Lastly, we sought to enhance protein production by introducing the plasmid containing the artificial origin (Fig. 1i) and employing the LoopReX-Opt system for final protein expression (experiments were performed using the single amplification scale-up method, Fig. 3c). We observed that combining these strategies further boosted protein production, resulting in a 2.5-fold increase in protein yield compared to the control (Fig. 3d). Our findings demonstrate that the plasmid with artificial origin significantly enhances DNA replication efficiency, as noted in Fig. 1i, and that the LoopReX-Opt system supports higher protein production than the traditional CFE system. Collectively, these results illustrate that the LoopReX-Opt system, incorporating the in vitro TTcDR framework, enables an efficient, scalable, and sustainable flow of genetic information.

In nature, eukaryotic and prokaryotic cells have their own ways of organizing and protecting DNA. The key difference lies in the presence of a membrane-bound nucleus in eukaryotes, whereas prokaryotes contain a membraneless nucleoid. Here, we aimed to construct an artificial nucleus-like structure to enhance the performance of in vitro TTcDR within complex biological systems. To achieve this, we selected the bacterial crystalline inclusion protein (CipB)48–50, which assembles spontaneously through hydrophobic interactions to form protein scaffolds, for the attachment of DNA sequences. Specifically, CipB was fused with streptavidin (i.e., CipB-streptavidin), enabling the attachment of biotin-modified linear double-stranded DNA (ldsDNA) (Fig. 4a). Since the resulting protein-DNA complex is not membrane-bound, we refer to it as “artificial nucleoid”.

We first characterized the formation of the artificial nucleoid via compartmentalization, using purified CipB-streptavidin and biotin-modified ldsDNA. Electrophoretic mobility shift assay confirmed that the biotin-modified DNA bound efficiently to the fused protein scaffold through the streptavidin-biotin interaction (Fig. 4b and Supplementary Fig. 10). Transmission electron microscopy revealed that the CipB-streptavidin monomers could assemble into a regular structure at the nanoscale (Fig. 4c, left and Supplementary Fig. 11). Upon adding DNA, the protein-DNA complex formed similar nano-compartments (Fig. 4c, right and Supplementary Fig. 11), resulting in artificial nucleoids. These structures further aggregated into protein-DNA clusters, visible as solid, green particles with FAM-labeled DNA (Fig. 4d). We next assessed the impact of CipB-streptavidin monomer concentration on DNA protection and observed that higher protein concentrations provided enhanced DNA protection (Supplementary Fig. 12). This effect is likely due to increased CipB concentrations promoting more efficient coating and compaction of DNA into artificial nucleoids, which in turn shields DNA from nuclease degradation in the cell extract, as reported in previous studies36,51–53.

To further investigate, we constructed artificial nucleoids using a ldsDNA template containing an artificial origin and the sfGFP gene (Fig. 4e, top). The template was modified with biotin at the forward (F) end, reverse (R) end, or both ends. We first evaluated the binding efficiency of DNA templates to artificial nucleoids and found that all biotin-modified templates bound with nearly 100% efficiency, whereas unmodified ldsDNA showed markedly lower binding efficiency, likely due to nonspecific interactions (Supplementary Fig. 13). With these various ldsDNA-loaded artificial nucleoids, we observed a significant improvement in TTcDR performance (Fig. 4e, f). For instance, the artificial nucleoid formed with dual-biotin-modified DNA showed a 7-fold increase in DNA replication efficiency, as evidenced by the greater amount of newly replicated DNA released into the supernatant (Fig. 4e; red bars). Additionally, this dual-biotin-modified DNA template exhibited superior DNA protection compared to the R-biotin-modified ldsDNA (Fig. 4e; orange bars). Notably, the highest protein expression was also achieved with the dual-biotin-DNA-formed artificial nucleoids (Fig. 4f). While the overall performance of TTcDR was improved with DNA-loaded artificial nucleoids, the efficiencies varied depending on the DNA–CipB binding format. Dual-biotin-modified DNA achieved the highest efficiency, as both ends were protected by CipB binding, preventing degradation and enabling more templates for protein synthesis. In contrast, single-biotin-modified DNA, with one free end exposed to nucleases, was more readily degraded, resulting in lower DNA replication and protein synthesis efficiency.

Due to the nature of our designed artificial nucleoids (which aggregate into larger structures), they can be easily collected via centrifugation and reused for subsequent rounds of reactions (Fig. 4g, top). Data demonstrated that artificial nucleoids could be reused for at least five cycles, maintaining substantial protein expression yields, with approximately 80% of the initial DNA remaining on the nucleoids (Fig. 4g). Taken together, these results highlight the advantages of artificial nucleoids, which not only mimic the compartmentalized structure of natural nuclei but also enhance the TTcDR performance.

Finally, we sought to realize the de novo, autonomous formation of artificial nucleoids in the LoopReX-Opt system by simply adding DNA as a single input. To test this concept, we first constructed a dual-biotin-modified ldsDNA template encoding the fused genes of CipB and mCherry. After cell-free expression, the fused CipB-mCherry protein self-assembled (self-growth) onto the pre-formed artificial nucleoids, resulting in a hybrid complex that exhibited red fluorescence (Fig. 5a). The red fluorescence increased progressively with each self-growth round (Fig. 5a), suggesting that newly expressed CipB-mCherry was incorporated into the artificial nucleoids, even though they did not carry DNA due to the absence of streptavidin.

Next, we added two dual-biotin-modified ldsDNA templates to the LoopReX-Opt system for the co-expression of CipB-streptavidin and CipB-LacZ. This approach enabled the de novo, autonomous formation of artificial nucleoids through the self-assembly of newly expressed CipB-streptavidin, CipB-LacZ, and the two ldsDNA templates (Fig. 5b). The resulting artificial nucleoid complex exhibited two key functions: CipB-streptavidin facilitated the attachment of the dual-biotin-modified ldsDNA template, while CipB-LacZ acted as an enzyme for bioconversion. The data showed that both dual-biotin-modified ldsDNA templates were successfully attached to CipB-streptavidin, which was encoded by one of the DNA templates (Fig. 5c). Moreover, after three rounds of self-growth, more CipB-LacZ was expressed and assembled, resulting in increasing catalytic activity for the conversion of o-nitrophenyl-β-D-galactopyranoside (ONPG) to o-nitrophenol (ONP) (Fig. 5d). Additionally, we successfully constructed a two-enzyme metabolic pathway by co-expressing StyA (the oxygenase subunit of styrene monooxygenase) and SpEH (epoxide hydrolase)27 with CipB-streptavidin, which self-assembled into artificial nucleoids and catalyzed the conversion of styrene into (S)-1-phenyl-1,2-ethanediol (Supplementary Fig. 14). These results collectively demonstrate that our LoopReX-Opt system is robust for the de novo, autonomous formation of artificial nucleoids, where multiple biological processes—such as DNA replication, protein expression, protein assembly, DNA attachment, and enzymatic catalysis (metabolism)—occur at different tempo-spatial scales. Remarkably, these essential cellular processes can be triggered in a single, one-pot LoopReX-Opt reaction with DNA as the sole user-supplied input.

Methods
Bacterial strains and media
The E. coli DH5α strain was utilized for molecular cloning and plasmid propagation, while E. coli BL21 Star (DE3) and its derivatives were employed for in vivo protein expression and preparation of cell extracts. For cultivation, E. coli was grown in LB (Luria-Bertani) medium, composed of 10 g/L tryptone, 5 g/L yeast extract, and 10 g/L sodium chloride. For cell extract preparation, cells were cultured in 2x YTPG medium, which includes 10 g/L yeast extract, 16 g/L tryptone, 5 g/L sodium chloride, 7 g/L potassium hydrogen phosphate, 3 g/L potassium dihydrogen phosphate, and 18 g/L glucose, with a final pH of 7.2.

Construction of expression templates
Coding sequences (CDSs) of phi29DNAP and streptavidin were codon-optimized and chemically synthesized by GENEWIZ (Suzhou, China). The CDSs are listed in Supplementary Data 1. For purification of phi29DNAP, a histidine-tag (6*his tag) was inserted at C-terminus of the phi29DNAP sequences and cloned into pET-28a. The plasmid pJL1-T7-sfGFP (Addgene #69496) used as a template for DNA replication and protein expression in CFE and LoopReX systems. All gene fragments were amplified by polymerase chain reaction (PCR). Then, the fragments were purified using the FastPure Gel DNA Extraction Mini Kit and ligated with the corresponding backbones using the ClonExpress Ultra One Step Cloning Kit (all purchased from Vazyme, Nanjing, China). The ligated products were then transformed into E. coli DH5α competent cells and grown on LB-agar plates containing the appropriate antibiotic for 12 h. After colony PCR, the correct samples were selected for further cultivation and plasmid extraction. DNA sequences were verified by Sanger sequencing (GENEWIZ, Suzhou, China). Additionally, all ldsDNA was cloned based on the pJL1 backbone, with biotin and FAM-labeled primers synthesized by GENEWIZ (Suzhou, China). To investigate the impact of transcript products on DNA replication, the φ10 promotor of pJL1-T7-sfGFP was replaced by φ13, φ1.1B and φ1.3. For the design of the artificial origin, the 1-3* φ10 promotor was inserted in situ based on the pJL1-T7-sfGFP. The promotor sequences are listed in Supplementary Data 1. All primer sequences used in this study are provided in Supplementary Data 2. Plasmids constructed in this work are listed in Supplementary Table 2.

In vivo protein expression and purification of phi29 DNA polymerase
E. coli BL21 Star (DE3) harboring the related plasmid was cultivated for protein expression. Seed culture was grown overnight in LB medium. Then, cells were used to inoculate 1 L of fresh LB medium with an initial OD600 of 0.05. When OD600 reached 0.6–0.8, cells were induced with 0.5 mM IPTG, followed by overnight cultivation at 220 rpm and 16 °C. Then, cells were harvested, and the pellets were washed twice with lysis buffer (40 mM Tris, 500 mM NaCl, and 10% glycerol, pH 8.0). After centrifugation at 5000×g and 4 °C for 10 min, cell pellets were resuspended with lysis buffer and lysed by sonication (50% amplitude, 10 s on/off for a total of 40 min). The supernatant was collected by centrifugation at 20,000×g and 4 °C for 30 min. The supernatant was collected and repeated once centrifugation. After collection, the supernatant was filtered using a 0.45 μm filter membrane and loaded onto a Ni Sepharose 6 Fast Flow column (Cytiva) for purification. Then, the column was washed five times with the lysis buffer containing gradient imidazole concentrations (0, 10, and 30 mM, respectively). After the final wash, the target proteins were eluted with elution buffer (40 mM Tris, 500 mM NaCl, 100 mM imidazole, and 10% glycerol, pH 8.0). Afterward, the eluted samples were desalted with the desalting buffer (40 mM Tris, 500 mM NaCl, and 10% glycerol, pH 8.0) using Amicon Ultra-15 centrifugal filters (10 kDa cutoff, Merck) by centrifugation at 5000×g and 4 °C. The concentrations of purified proteins were determined using a Quick Start Bradford Protein Assay kit (Bio-Rad). The purified products were stored at –80 °C until use.

Replication assay with single-stranded M13mp18 DNA in reaction buffer
For DNA replication assay based on phi29DNAP, single-stranded M13mp18 DNA (ssM13 DNA, NEB) was hybridized with a 17-mer oligonucleotide primer. The incubation mixture (20 μL) contained 50 mM Tris-HCl, 10 mM magnesium chloride, 10 mM ammonium sulfate, 4 mM dithiothreitol (DTT), 0.2 mg/mL recombinant bovine serum albumin, 0.3 mM dNTPs, 5 nM ssM13 DNA, 25 nM oligonucleotide primer (Supplementary Data 2), and phi29DNAP. The effect of different phi29DNAP concentrations on DNA replication was examined in this experiment. ssM13 DNA and primers were preincubated at 65 °C for 2 min and subsequently cooled to 25 °C. After incubation for the indicated times at 30 °C, samples were analyzed by electrophoresis in 1.0% agarose gels alongside DNA markers (Trans2K® Plus II DNA Marker) and by real-time quantitative PCR. The results showed that adding 10 μM phi29DNAP to the reaction generated large-molecular-weight rolled-circle concatemer products (Supplementary Figs. 1 and 2). Therefore, subsequent experiments were performed with the concentration of 10 μM phi29DNAP.

Gel analysis of DNA replication products
DNA replication samples were separated by agarose gel electrophoresis in 1× TAE buffer (Tris-Acetate-EDTA) and stained with YeaRed Nucleic Acid Gel Stain (Yeasen, Shanghai, China) to visualize the replication products. A fraction of the total product remained in the gel pockets, likely due to the large size of some rolling-circle concatemers and/or the potential formation of MgPPi-DNA nanoparticles7.

Cell extract preparation
Cell growth, collection, and extracts were prepared as described previously34,35. In brief, all E. coli strains were cultured in 2x YTPG medium. Each 1 L culture was inoculated with an overnight preculture at an initial OD600 of 0.05. When the OD600 reached 0.6–0.8, 1 mM IPTG was added to induce T7 RNA polymerase expression, and cells were harvested at an OD600 of 3.0. The cells were washed three times with cold S30 Buffer (10 mM Tris-acetate, 14 mM magnesium acetate, and 60 mM potassium acetate). After the final wash and centrifugation, the cell pellet was resuspended in S30 Buffer (1 mL/g wet cell mass) and lysed by sonication (10 s on/off, 50% amplitude, input energy ~600 J). The lysate was then centrifuged twice at 12,000×g for 10 min at 4 °C. The supernatant was flash-frozen in liquid nitrogen and stored at −80 °C until use.

Cell extract-based in vitro DNA replication and gene expression reaction
CFE reactions for DNA replication and gene expression were performed in 1.5 mL microcentrifuge tubes. A standard reaction (15 μL) contained the following components: 12 mM magnesium glutamate, 10 mM ammonium glutamate, 130 mM potassium glutamate, 2.64 mM ATP, 1.89 mM each of GTP, UTP, and CTP, 74.8 μg/mL folinic acid, 375.3 μg/mL of E. coli tRNA mixture, 2 mM each of 20 standard amino acids, 0.4 mM nicotinamide adenine dinucleotide (NAD), 0.27 mM coenzyme A (CoA), 1.5 mM spermidine, 1 mM putresputrescine, 4 mM sodium oxalate, 33 mM phosphoenolpyruvate, 0.3 mM dNTPs, 1 nM pJL1-T7-sfGFP or 5 nM ssM13 and 25 nM oligonucleotide primer, and 27% (v/v) of cell extract. For the LoopReX reaction, an additional 10 μM phi29DNAP is required. All reactions were incubated at 30 °C before further analysis of the synthesized DNA and protein. To verify the relationship between DNA replication efficiency and T7RNAP, 27% (v/v) of cell extract without IPTG induction and 20-40 μM T7RNAP are required.

Relative DNA quantification by qPCR
Fold changes of DNA copy-number relative to input levels (t=0) were measured by qPCR (ChamQ SYBR Color qPCR Master Mix, Vazyme) in a two-step Real-Time PCR method (CFX96TM Real-Time System, Bio-Rad). For each time point, three individual samples were taken and diluted to the appropriate fold for DNA template in ddH2O, which were further diluted 1:20 in the final qPCR reaction. In order to reduce the adverse effect of the random error of the experimenter’s operation on the machine learning of a large amount of data, the dilution process and qPCR sample loading process were completed by an automated liquid handling workstation (Agilent BRAVO liquid handler). The specific primers for each target amplicon are listed in Supplementary Data 2. The fold change is defined as DNA content at that time/DNA input, where “DNA content at that time” and “DNA input” were both detected by qPCR in each independent experiment. The qPCR standard curves involved in this experiment are listed in Supplementary Figs. 17–23.

Protein expression assay
In this experiment, the expression of sfGFP was uniformly used to represent the protein expression level. After cell-free reactions, the fluorescence of sfGFP was measured using a microplate reader (Synergy H1, BioTek). Briefly, 2 μL of the reaction sample was mixed with 48 μL of nuclease-free water and placed in a 96-well plate with a flat bottom. Then, measurements of the sfGFP fluorescence were performed with excitation and emission wavelengths at 485 and 528 nm, respectively. Finally, the fluorescence of sfGFP was converted to concentration (μg/mL) or total yield (μg or μg/nM) according to a linear standard curve listed in Supplementary Fig. 24.

Factor selection and data processing
The essential functions of CFE systems are derived from cell extracts. To sustain high transcription and translation activity outside the cellular environment, traditional cell-free reactions typically incorporate over 50 supplementary components. These components have been shown to influence DNA replication and protein expression through various mechanisms7,9,12. Additionally, within the complex milieu of cell extracts, interactions among these factors are expected. Therefore, we collected data on protein expression and DNA replication in our laboratory involving 16 factors in the standard protocol of CFE, including magnesium glutamate, ammonium glutamate, potassium glutamate, ATP, GTP, UTP, CTP, folinic acid, E. coli tRNA mixture, NAD, CoA, spermidine, putrescine, sodium oxalate, dNTPs, and DTT. To reduce noise and errors in the experimental data and improve the accuracy and reliability of the analysis, the Robustness Analysis method (Coefficient of Variation, CV) is used. 126 sets of effective data related to protein expression and 115 sets of effective data related to DNA replication were obtained, respectively.

Furthermore, for parallel experiments in the collected data, Z-score filtering was applied to detect and remove outliers from the experimental data. A threshold of Z=1.5 was set, and data points with Z-scores exceeding 1.5 or less than –1.5 were filtered out. The step of computing the Z-score for each data point is as follows (Eq. 1):

Z = (X - μ) / σ (1)

where Z is the Z-score of data X, μ is the mean of the data point, and σ is the standard deviation. Then, to make the data more suitable for subsequent analysis, the filtered data was standardized using the Z-score normalization method, transforming the data to a distribution with a mean of 0 and a standard deviation of 3. To meet specific experimental requirements, the standardized data were further corrected to a range of 0–6 to ensure all data points are positive. Since DNA replication and protein expression were theoretically positive results, they were further corrected to a range of 0–6 to ensure that all data points are positive values for subsequent model construction.

Model development
After data processing, we utilized eXtreme Gradient Boosting (XGBoost), an open-source library that efficiently implements the gradient boosting framework, to build the protein expression predictive model and the DNA replication predictive model, respectively. Gradient boosting reduces prediction error using a gradient descent algorithm and creates a model composed of multiple weak prediction models, specifically decision trees in this context. During training, new regression trees were added sequentially to minimize residuals (the difference between predicted and actual values), while existing trees remained unchanged to help prevent overfitting. The outputs of new trees were combined with those of existing trees until the loss was minimized to a certain threshold or the specified limit on the number of trees was reached. In this study, an XGBoost regressor, implemented through Python code, was trained for each predictor and for the full set of data to learn and understand how many concentrations of factors can influence the efficiency of protein expression and DNA replication. All the data was divided at random into training and test sets with a ratio of 8:2. Only the training set was applied in model training and hyperparameter tuning. To improve the robustness of the model, five-fold cross-validation was used to optimize the model hyperparameter using grid search over the range of possible hyperparameter values. The test set was reserved for testing the performance of the models. Prediction performance was measured using R2; the higher the value of R2, the more accurate the model.

In this study, we employed machine learning to model DNA replication and protein expression within the LoopReX system, allowing us to identify conditions that optimize both processes simultaneously. Specifically, to find the best conditions for improving protein expression and DNA replication efficiency, we traversed millions of combinations of conditions across the data range. This exhaustive search was designed to rapidly determine the conditions that maximize protein expression and DNA replication efficiency.

Iterative in vitro DNA replication and gene expression
To validate the DNA iterative replication capacity of the LoopReX-Opt system, we prepared a reaction using the F3 formulation according to the standard CFE protocol (15 μL, named standard LoopReX-Opt protocol), incubating it at 30 °C for 1 h as the seed solution. Subsequently, 5 μL of the seed solution was transferred into a fresh standard LoopReX-Opt reaction (15 μL), bringing the total volume to 20 μL, and incubated again at 30 °C for 1 h for the first iteration. This process was repeated, and DNA replication capacity was sustained through the fourth iteration.

For saturated iterative reactions, the entire reaction mixture from the previous round was used as the seed and combined with the fresh reaction mix for the next round, following the ratio of 1:3 (seed/fresh reaction). Since LoopReX reaches peak protein expression and maintains high DNA levels within 3 h, the iteration time was set at 3 h. Briefly, after a 15 μL standard LoopReX reaction incubated at 30 °C for 3 h, three aliquots of 5 μL were transferred into three fresh 15 μL reactions to begin the first iteration. Starting from this first round, each reaction has a total volume of 20 μL, allowing one 20 μL seed reaction to generate four subsequent reactions. In the second iteration, this process can expand into 12 reactions, and so on. The number of iterations (n) and reaction number (N) follow the formula (Eq. 2):

N = 3 * 4^(n-1) (2)

When calculating cumulative protein expression for each iteration, we minimized experimental workload by conducting only the minimal unit for each iteration—transferring one tube of seed solution to three reaction tubes in each round. The cumulative protein expression for each iteration follows the formula (Eqs. 3 and 4):

P = AVERAGE [Σ (f(A) * V)] (3)

A = N * (F3 - F0) (4)

where: P is the total cumulative protein expression; f(x) is the standard curve of sfGFP; V is the reaction volume; r is the reaction tube in each round; F3 is the protein fluorescence value at 3 h and F0 is the background value; N is the theoretical reaction number.

For volume-scaled-up iterative reactions, we adjusted the overall experimental design to align with cell culture protocols, leading to significant differences compared to saturated iterations. Three experimental groups were designed: no iteration, one scale-up iteration, and two scale-up iterations, with approximately 10-fold gradient increases in volume (15 μL, 110 μL, and 1100 μL). Each group’s final round was used for protein expression, incubated at 30 °C for 6 h. The remaining samples served as seed solutions for DNA replication, incubated at 30 °C for 1 h. Fresh reaction volumes were scaled up proportionally to the standard 15 μL protocol. For the first iteration, 10 μL of seed solution was transferred to a fresh 100 μL system and reacted at 600 rpm in a shaker (Thermomixer) to enhance oxygenation and maintain homogeneity. For the second iteration, 100 μL of seed solution was transferred to a fresh 1 mL system, reacting at 250 rpm in an orbital incubator (Infors-HT). All control experiments were performed using CFE system as seed or reaction solution.

Preparation and recovery of artificial nucleoids
Except for the de novo synthesis strategy of artificial nucleoids within the LoopReX system, other related experiments used pre-prepared artificial nucleoids. To prepare artificial nucleoids, the structural monomers (CipB-streptavidin) were expressed in 200 μL of CFE system containing the expression plasmids (pJL1-φ10-cipB-streptavidin), which were incubated at 30 °C and 600 rpm in shaker for 10 h. At the end of the reaction, artificial nucleoids were harvested by centrifugation at 20,000×g for 10 min at 4 °C. The nucleoid pellets were first washed and resuspended in PBS buffer (containing 1 mM EDTA, 1% Triton X-100, pH 7.4) at a 10-fold sample volume, followed by two additional washes and resuspensions with PBS buffer (pH 7.4) at the same 10-fold sample volume. The purified artificial nucleoids were then collected by centrifugation at 20,000×g for 10 min at 4 °C and resuspended in nuclease-free water.

For concentration determination, the washed samples were dissolved in 2 M urea, and the protein concentration was measured using a UV-Vis micro-spectrophotometer (Nano-300, Hangzhou Allsheng Instruments, China) with absorbance at 280 nm (A280). Protein concentrations (mg/mL) were calculated by dividing the A280 value by the extinction coefficient. Next, the artificial nucleoid was mixed with biotin-labeled ldsDNA at an appropriate ratio, followed by incubation in DNA loading buffer (10 mM Tris-HCl, 1 mM EDTA, 0.1 M NaCl, 0.1 mM DTT, 10 μg/mL BSA, and 0.5% glycerol) at 25 °C for 30 min. After incubation, the mixture was washed twice with PBS (pH 7.4) buffer at a 10-fold sample volume. Finally, PBS was removed by centrifugation, and the resulting nucleoid pellet was resuspended with nuclease-free water to obtain the loading DNA artificial nucleoid.

In iterative reactions, artificial nucleoids (serving as the replication core) were collected after each cycle by centrifugation at 20,000×g and 4 °C for 10 min. The nucleoid pellets were washed twice with PBS (pH 7.4) buffer at a 10-fold sample volume and then resuspended in nuclease-free water at an appropriate volume for the next round of reactions.

Transmission electron microscopy (TEM)
TEM images were acquired using a JEOL JEM-1400 Plus microscope at an operating voltage of 120 kV. Samples (10 μL of artificial nucleoids resuspended in nuclease-free water) were deposited on a carbon-coated copper grid (300 mesh), excess liquid was removed by filter paper after 1 min, and the grids were air-dried. Negative staining was performed with 2% uranyl acetate (10 μL, 30 s), followed by air-drying before imaging.

Catalysis assay of functionalized artificial nucleoids
We functionalized the artificial nucleoid by adding functional monomers (CipB-LacZ), with LacZ catalysis used to simulate the metabolic function of the artificial nucleoid. The enzymatic reactions were carried out in 1.5 mL tubes by adding ONPG as the substrate. After the de novo synthesis of the functionalized artificial nucleoid in the one-step reaction, the functionalized artificial nucleoid was harvested and washed. The functionalized artificial nucleoid was resuspended in 10 μL of nuclease-free water and transferred to 190 μL of reaction mixture containing 10 mM ONPG and 1 mM MgSO4 at 25 °C for 1 min. Then, 200 μL of termination buffer (1 M Na2CO3 and 0.1 M EDTA) was added to stop the reaction. The absorbance of the product ONP in the reaction mixture was measured using a microplate reader (Synergy H1, BioTek) at 420 nm. The absorbance of ONP was converted to concentration (mM) according to a linear standard curve listed in Supplementary Fig. 25. The conversion was calculated using the following formula (Eq. 5):

Conversion = (Cafter / Cbefore) * 100% (5)

where Cafter represents the concentration of ONP and Cbefore represents the initial concentration of ONPG added to the reaction. Comparison of enzymatic activities and structural predictions of CipB-LacZ versus LacZ is provided in Supplementary Figs. 26 and 27, respectively.

Statistical analysis
All experiments were performed in biological triplicates, unless otherwise specified. Data are presented as means, with error bars representing the standard deviation from three independent experiments (n=3). Statistical analyses were conducted using two-tailed Student’s t tests. Statistical significance is denoted as follows: p<0.01 (**), p<0.001 (***), and p<0.0001 (****); exact p-values are provided in the corresponding figure legends.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript entitled “Reconstituting artificial central dogma within complex in vitro biological systems” by Xiao Zheng et al. describes a new reaction system called LoopReX, which was also conducted in a CipB-based nucleoid-like system. First, they established the LoopReX system by combining phi29 DNA polymerase with a cell-free extract of E. coli and obtained some important insights into the initiation mechanism. They also optimized the composition of the components using machine learning techniques. Second, serial dilution experiments showed that the presence of LoopReX enhanced protein expression. Third, they attached a template DNA with a CipB-based aggregate to mimic nucleoids. They showed that the nucleoid-like structure increased resistance to degradation and enhanced DNA replication. Finally, they combined the nucleoid-like structure with lacZ-encoding DNA to demonstrate the functionalization and reusability of the structure. This study includes a large number of experiments. I respect the efforts of the authors. I also think This study also tackled an important challenge in the field of in vitro synthetic biology. However, I think that this manuscript has some serious issues to be addressed before publication, as listed below.\nMajor issues.\n5.1 Page 6, “(i) successful reconstitution of the artificial central dogma using…”. Again, I believe the authors should define the minimum requirement for an artificial central dogma..\n5.2 Page 7, “…enabling the construction of an efficient, minimal replication unit for LoopRex.” The author did not explain why they believe it is “mimnimal”\n5.3 Page 12 “This demonstrates that LoopReX-Opt enables robust DNA replication across generations, mimicking the transfer of genetic information during cell proliferation.” The experiment conducted was a serial dilution experiment. There is no cell, no cell division, and no segregation. In addition, the DNA replication scheme used in this study is not recursive. I believe that these data do not mimic the transfer of genetic information during cell proliferation.\n6.1 The last sentence on page 3, “When the number of initiation sites…,” is not understandable. The author mentioned that the number of initiation site dropped and the accumulation of pyrophosphate, DNA polymerization decreased, but the author did not provide any data to support this. Also the author wrote that the exonuclease activity remained stable, but they did not measure exonuclease activity. Please describe the result more precisely.\n6.2 In the sentence on page 4, “We then replaced the cssDNA…”, the author wrote “successful replication of pJL1-T7-sfGFP,” but replication was just 1.3 fold. How did the authors conclude that this was successful?\n6.3 In the sentence in page 4, “However, the DNA content in …”, the author wrote “with DNA replication supporting the template synthesis continuously.” I did not understand the logic of the author. Certainly, the fold changes were higher for LoopRex system at all points in Fig 1c, but this can be because DNA replicated for LoopRex system before 0.5 h and then DNA might stop replication and just degraded afterward. In this case, template synthesis (i.e., DNA replication) did not proceed continuously.\n6.4 The last sentence on page 5 contains the word “random,” which probably mentions the nucleic acids present in the cell extract. I think that nucleic acids should be rRNA, tRNA, mRNA, and fragments from the genome. They are never random. Please rephrase it. More importantly, I could not understand the conclusion of Fig. S4, “the nucleic acids present in the cell extract still effectively supported the synthesis of new DNA strand.” To draw this conclusion, the author would need DNA replication experiment with or without the nucleic acid in the cell extract, but there is no such data. Initially, I thought Fig. S4C was the data, but the author wrote in the legend that the primer used there was a specific primer, not the nucleic acids in the extract. Accordingly, I could not agree with the first sentence on page 6, “Our findings indicate that ….” Overall, this manuscript lacks explanation of the data, especially in the supplementary figures. I strongly recommend that the authors provide a more detailed explanation of the Supplementary Figures.\n6.5 Page 8. What is ZGBoost models. A brief explanation is needed in the main text.\n6.6 Machine learning part. The explanation of the dataset used for machine learning was insufficient. Please explain how the author got the data. Which factors changed? How much did they change? How much data did they get?\nMinor points\n1. Figure S1. How to stain DNA?\n2. Figure S2 How to measure ssM13 replication?", "reviewer_2": "This article presents a thorough proof of concept that a minimal circuit for DNA replication can be designed and iterated upon in E. coli lysate. Trying to expand cell free to beyond just in vitro transcription and translation to extend to replication is conceptually useful, and this is a thoroughly completed proof of concept involving machine learning, demonstration of improved DNA replication, and compartmentalization, and demonstration that it can improve the concentration of reporter proteins.\nA few comments, that are mostly high level, but I think a large re-write adjusting the framing is necessary for publication.\n1. Framing it as “artificial central dogma” complete obfuscates the entire premise of the article. Canonically, the central dogma refers to the flow of information in cells (DNA-->RNA-->Proteins) which certainly happens in any lysate or PURExpress-based TXTL system wherein transcription and translation are necessary to transfer information. Replication is important for cells, but I don’t think this is generally what people think of when they consider the central dogma of biology. For this reason I think “reconstituting artificial central dogma” feels confusing and somewhat jargon-y and makes it harder for the reader to digest this information. I think a concept of the central dogma that includes regeneration and self-replication has come into dialogue in part of the synthetic biology/origins of life/artificial cells community, but it is not widely used language in the broader synthetic biology community, much other areas of molecular biology.\nI recommend that throughout the manuscript, “artificial central dogma” is replaced with more precise language. I also recommend a title change\n2. Related—on page 2 there is a discussion of the PURExpress system and its limitations:\n“Elements)-based cell-free expression system, which operates within a well-defined (bio)chemical environment6 , has been widely used to construct artificial life systems7-9 . However, these systems often exhibit only individual life characteristics, for example, sel freplication (e.g., DNA replication)7-12 , self-evolution13-15 , cell division16-18 , communication19-23 , or metabolism25 . Essentially, PURE system represents a relatively simplified platform for in vitro biology.”\nI may be misunderstanding the point of this sentence but how can PURExpress systems replicate cell division outside of the cell. I believe the point was to say that individual components of life can be replicated, but it is challenging to put it all together, but I would recommend a major rewrite of this section for clarity.\n3. Overall, I caveat the authors on boldly claiming concepts are “confirmed” when they are merely supported. An example of this can be found on page 6\n“These findings confirm our hypothesis that the position of the promoters (i.e., the artificial origin) plays a crucial role in regulating DNA replication and transcription”\nI think the word “support” would be better than “confirm”. Given the toy system nature of this investigation, it is difficult to say how it reacts in more complex situations. It’s just one piece of evidence supporting this hypothesis.\nAnother example of this can be found on page 20. Did the authors indicate that compartmentalization is “essential”. I feel this work just shows that it is beneficial and improves protein production, not that this cannot occur without it. The controls demonstrate that this replication can occur, just less efficiently\n4. In Supplementary Figure 10 and Figure 4c could use a clearer explanation of why morphology observed for DNA complexes is lighter and what the TEM is measuring, and perhaps a reference for the method—essentially since this is such an interdisciplinary field, it should walk the reader through these experiments\n5. While the machine learning checklist says there are instructions for how to obtain code, I do not see any statement about this written in the text, e.g. if the authors intend to deposit Juptyr notebooks on GitHub", "reviewer_3": "The manuscript “Reconstituting artificial central dogma within complex in vitro biological systems” demonstrated the central law in a complex in vitro system for the first time, which provides a new idea for the construction of artificial life system. I still have some comments that should be addressed prior to publication of this manuscript. The points would need to be addressed as follows.\n1. Although the study revealed the synergistic effect of T7RNAP and phi29DNAP in DNA replication, the specific molecular mechanism remains to be further explored. It is suggested that biochemical analysis or structural biology methods should be used to further elucidate the specific roles of these enzymes in the replication process. For example, ‘forming a replication initiation complex for DNA replication’ (Fig.1g), the complex should be considered.\n2. The current system is mainly focused on DNA replication and protein expression, and more cellular functions such as metabolic pathways and signal transduction can be considered.\n3. Besides being faster, what other advantages does LoopReX have? Where does more DNA than CFE flow? (Fig.1d)\n4.In machine learning, is the division of the training set and the test set random? What is the basis of the division?\n5.In Figure 3b, the increase of DNA mass is due to the continuous enlargement of the system or the continuous iteration of culture?\n6. In Figure 3c, what are the key differences between in vitro amplification and small trials? Is the effect equal amplification?\n7. Does artificial nucleoid perform other functions, such as signal transduction? What are the properties of artificial nucleoid, solid or liquid?\n8. What is the reason of ‘higher protein concentrations provided enhanced DNA protection’?\n9. In Figure 4e and 4f, the efficiency of DNA and protein synthesis is not quite the same, what are the possible reasons?\n10. How can it be shown that DNA replication occurs on a nucleoid and transcription occurs in liquid?\n11. In Figure 5a, how can we undo the effect of non-karyon expression in vivo?\n12. Is it the order of transcription and then replication in vitro? Is the translation process sequential? Is there any representation of translation? Is it transcription and translation? What if it's a eukaryotic system?\n13. What are the advantages of scaling up protein production compared to in vivo, in terms of cost and time?\n14. What are the intracellular and extracellular aggregation conditions of CipB? Which conditions can achieve the best aggregation effect?\n15. Whether the activity of the enzyme or protein after aggregation with cipB has been changed? Whether the folding of the protein or enzyme has been affected from soluble to insoluble? Authors should include some predictive data on the effect of CipB on the structure of the target enzyme or protein, such as using AlphaFold3."}, "author_response": "Point-by-point response to reviewer comments:\nComments in italics\nResponses in blue\nChanges in the body of the manuscript are noted in red.\nReviewer Comments\nReviewer #1 (Remarks to the Author):\nThe manuscript entitled “Reconstituting artificial central dogma within complex in vitro\nbiological systems” by Xiao Zheng et al. describes a new reaction system called\nLoopReX, which was also conducted in a CipB-based nucleoid-like system. First, they\nestablished the LoopReX system by combining phi29 DNA polymerase with a cell-free\nextract of E. coli and obtained some important insights into the initiation mechanism.\nThey also optimized the composition of the components using machine learning\ntechniques. Second, serial dilution experiments showed that the presence of LoopReX\nenhanced protein expression. Third, they attached a template DNA with a CipB-based\naggregate to mimic nucleoids. They showed that the nucleoid-like structure increased\nresistance to degradation and enhanced DNA replication. Finally, they combined the\nnucleoid-like structure with lacZ-encoding DNA to demonstrate the functionalization\nand reusability of the structure. This study includes a large number of experiments. I\nrespect the efforts of the authors. I also think This study also tackled an important\nchallenge in the field of in vitro synthetic biology. However, I think that this manuscript\nhas some serious issues to be addressed before publication, as listed below.\nResponse: We thank you for your thoughtful and thorough evaluation of our manuscript.\nWe are delighted that you recognized the significance of our work in addressing an\nimportant challenge in the field of in vitro synthetic biology. We greatly appreciate your\nconstructive comments and have carefully considered and addressed each of your\nconcerns point-by-point below.\nMajor issues.\n1. The novelty of this study is still not fully understood. I believe that the main\nconclusion is the reconstitution of the artificial central dogma, as written in the title and\nabstract. In my understanding, which I believe is general, the central dogma is the\nflow of information in the cell, from DNA to RNA, and then proteins. This flow has been\nalready implemented in a typical cell-free extract. The new point of this study is the\nintroduction of a DNA replication scheme into cell-free extracts. I think that the use of\nthe word ‘central dogma’ does not fully match the achievements of this study. I think\nmore specific word, such as DNA replication, should be used, but if the authors insist\nto use the word “central dogma”, they should define it before use.\nResponse: Thank you for your thoughtful and constructive comments. We agree that\nthe canonical definition of the central dogma refers specifically to the unidirectional\nflow of genetic information from DNA to RNA to proteins. In light of your suggestion,\nwe have revised the manuscript to replace the term “artificial central dogma” with more\nprecise and accurate descriptions such as “transcription–translation-coupled DNA\nreplication (TTcDR)”, which more clearly reflects the novelty and scope of our study.\nAdditionally, we have revised the manuscript title to “Reconstituting transcription–\ntranslation-coupled DNA replication within complex in vitro biological systems”, in line\nwith the request from Reviewer 2 (Comment 1). We hope these revisions address your\nconcerns and improve the clarity and impact of our work.\n2. Moreover, I felt that the initial part of this study (Figs. 1-3) was not impactful because\nthe replication fold was low (only 1-3 fold) and optimization using machine learning\nwas not as effective (both DNA replication and GFP expression increased slightly) (Fig.\n2g and 2h). In contrast, the effect of the nucleoid (Fig. 4e) was more pronounced. I\nthink it might be better if the author emphasizes the result in the nucleoid in the title\nand conclusion.\nResponse: Thank you for your valuable comments and for highlighting the strength of\nthe nucleoid-based system in our study. We acknowledge that the DNA replication fold\nobserved in the initial part (Figs. 1-3) is modest. However, we think this phase as an\nessential foundation for demonstrating the feasibility of in vitro DNA replication and for\nestablishing the LoopReX system. The machine learning-based optimization, while\nyielding incremental improvements in DNA replication and gene expression, served to\nsystematically explore the complex multivariable space, laying the groundwork for the\nsubsequent improvements achieved via compartmentalization. Importantly, machine\nlearning optimization markedly accelerated protein synthesis. Using the same initial\nDNA concentration, the optimized system reached its maximum protein yield in only 3\nhours, which is over twice as fast as the pre-optimized system (8 hours; Fig. 2h) and\nnearly five times faster than a conventional cell-free expression system (14 hours;\nSupplementary Fig. 9). This substantial improvement highlights the efficiency gains\nachieved through our optimization approach.\nWe agree that the nucleoid-mimicking structure introduced in Fig. 4 represents a major\nadvancement in our study, as it significantly enhanced DNA replication and stability.\nThanks for your suggestion. As noted in our response to Comment 1, we have revised\nthe title to better reflect the overall contributions of our work. In addition, we have added\nthe following text to the discussion/conclusion to more clearly emphasize the impact\nand significance of the nucleoid-based system (Page 22).\n“As such, DNA templates could be well protected from degradation (Supplementary\nFig. 12), and importantly, the DNA replication efficiency was increased up to 7-fold\n(Fig. 4e) compared to LoopReX reactions without the incorporation of artificial\nnucleoids (less than 3-fold as observed in Figs. 1 and 2).”\n3. The DNA replication conducted in this study is not recursive; the product is not the\nsame as the template, in my understanding. This is not the same as usual natural\nDNA replication. The author should indicate this point somewhere in the text.\nResponse: We thank you for raising this point of clarification. You are correct that the\nDNA replication observed in our system is non-recursive, as the amplified products are\nnot identical to the original circular DAN templates. Instead, the replication proceeds\nvia phi29DNAP-based rolling circle amplification, producing concatemeric linear DNA\nrather than discrete circular products (Supplementary Fig. 1).\nTo make this point clear, we have added the following text in the revised manuscript\n(Page 4):\n“It is important to note that the DNA replication in our system proceeds via phi29DNAP-\nbased rolling circle amplification and is therefore non-recursive—the replicated DNA\ndoes not recapitulate the original circular template. This differs from canonical\nchromosomal replication in vivo, but still provides a powerful and programmable\nmeans of DNA amplification and template regeneration in vitro7-12.”\n4. The novelty of this study is the introduction of DNA replication in a cell-free extract\nsystem by supplying purified DNA polymerase. However, as the authors cited in the\nIntroduction section, DNA replication has been introduced in a reconstituted system\n(PURE system), in which DNA polymerase is expressed from the replicated DNA. I\nthink the authors should clearly describe the difference between their system and the\nprevious reconstituted system and the advantages of their system.\nResponse: Thank you for raising this important point. To clarify the distinction, we have\nnow highlighted the key differences and advantages of our approach in the revised\nmanuscript. In particular, our system (1) uses an E. coli cell-free extract rather than a\nreconstituted system like PURE, which provides a richer metabolic and enzymatic\nbackground for replication, transcription, and translation; (2) incorporates externally\nsupplied DNA polymerase, which allows for immediate and robust replication without\nrelying on cell-free synthesis of the polymerase itself; and (3) integrates replication\nwith transcription and translation in a compartmentalized system (e.g., nucleoid-like\nstructures), which enhances performance and opens new avenues for studying spatial\norganization in synthetic biology.\nTo reflect this more clearly, we have added the following text to the Introduction (Page\n3):\n“In contrast to previous reconstituted systems such as PURE7-9, where replication\nmachinery is encoded and expressed in situ, our approach uses a cell extract-based\nsystem supplemented with purified DNA polymerase. This enables robust, immediate\nreplication and provides a versatile platform for tuning replication independently of\nprotein synthesis, while leveraging the complex biochemical environment of the lysate.”\nFurthermore, in paragraphs 4-5 of the Discussion, we discussed and emphasized the\nadvantages of our system in comparison with earlier systems (Pages 22-23).\n5. I feel that several parts of the description in this manuscript are not precise and are\nnot suitable for a scientific paper. Below are the points I feel are not concise.\n5.1. Page 6, “(i) successful reconstitution of the artificial central dogma using…”. Again,\nI believe the authors should define the minimum requirement for an artificial central\ndogma.\nResponse: Thank you for this comment. As you and Reviewer 2 noted, the phrase\n“artificial central dogma” can be misleading without a clear definition. To avoid\nambiguity, we have removed this term from the revised manuscript and replaced it with\nthe more precise phrase “transcription–translation-coupled DNA replication (TTcDR)”,\nthereby avoiding to redefine an extended version of the central dogma.\nAccordingly, the sentence has been revised as follows (Page 7):\n“(i) successful reconstitution of TTcDR in vitro using phi29DNAP and T7RNAP in a cell\nextract-based CFE system…”\n5.2. Page 7, “…enabling the construction of an efficient, minimal replication unit for\nLoopRex.” The author did not explain why they believe it is “mimnimal”.\nResponse: We appreciate your observation and agree that the term “minimal” requires\nclarification. In our original phrasing, we intended to emphasize that the replication unit\nwe developed is functionally minimal in that it comprises only two essential enzymes\n(phi29DNAP and T7RNAP) for DNA replication in our system.\nTo clarify this point, we have revised the sentence as follows (Page 7):\n“…enabling the construction of an efficient, two enzymes-based replication unit for\nLoopReX.”\n5.3. Page 12 “This demonstrates that LoopReX-Opt enables robust DNA replication\nacross generations, mimicking the transfer of genetic information during cell\nproliferation.” The experiment conducted was a serial dilution experiment. There is\nno cell, no cell division, and no segregation. In addition, the DNA replication scheme used\nin this study is not recursive. I believe that these data do not mimic the transfer of\ngenetic information during cell proliferation.\nResponse: We thank you for pointing out this important distinction. We agree that our\nsystem lacks key biological features of cell proliferation, such as compartmentalized\nsegregation, recursive replication, and actual cell division. The original sentence was\nintended to convey that the serial dilution experiment demonstrated sustained DNA\nreplication activity and template inheritance in a biochemical sense, but we recognize\nthat describing this as “mimicking the transfer of genetic information during cell\nproliferation” could be misleading or overstated.\nTo make this point more precise, we have removed the reference to cell proliferation\nand revised the sentence as follows (Page 13):\n“This demonstrates that LoopReX-Opt enables sustained DNA amplification across\nserial dilutions, supporting template persistence over multiple reaction cycles.”\n6. The manuscript also contains some ununderstandable parts, probably because of\ninsufficient explanation. The authors should add more explanation.\n6.1. The last sentence on page 3, “When the number of initiation sites…,” is not\nunderstandable. The author mentioned that the number of initiation site dropped and\nthe accumulation of pyrophosphate, DNA polymerization decreased, but the author did\nnot provide any data to support this. Also the author wrote that the exonuclease activity\nremained stable, but they did not measure exonuclease activity. Please describe the\nresult more precisely.\nResponse: Thank you for raising this point of clarification. Our original sentence was\nintended as a plausible explanation for the observed DNA amplification behavior, but\nwe acknowledge that it implied conclusions beyond the data provided. To address this,\nwe performed additional experiments during the revision to directly examine (i) the\nexonuclease activity of phi29DNAP, and (ii) the effects of the replication initiation\ncomplex (circular single-stranded DNA + initiation primer) and pyrophosphate on DNA\npolymerization. The results are now included in Supplementary Figure 4 (as shown\nbelow in Figure R1).\nFirst, phi29DNAP exhibited clear exonuclease activity. ssM13 DNA was completely\ndegraded within 2 h in the absence of initiation primers and dNTPs (reaction setup\ndescribed in Methods). Second, DNA replication efficiency depended strongly on the\nconcentration of the replication initiation complex present in the reaction. Third,\nsupplementing the reaction with additional pyrophosphate did impact DNA replication\nefficiency, which is consistent with previous reports (Dean et al., Genome Res. 2001,\n11, 1095-1099; Seo et al., ACS Synth Biol. 2023, 12, 1813-1822). This is likely due to\npyrophosphate chelating magnesium ions, thereby influencing phi29DNAP’s\npolymerization and degradation activities. For this reason, we evaluated the impact of\npyrophosphate using “net DNA synthesis activity” as a combined measure of\npolymerization and degradation (see below Figure R1 legend for details).\nTo make this point clear, we have revised the text to more accurately reflect our\nobservations as follows (Pages 3-4):\n“However, DNA replication efficiency declined over time (Supplementary Fig. 3),\nlikely due to phi29DNAP’s exonuclease activity, depletion of replication initiation\ncomplexes (circular single-stranded DNA + initiation primer), and accumulation of\ninhibitory byproducts such as pyrophosphate (Supplementary Fig. 4)7,12,37,39.”\nFigure R1. Evaluation of factors affecting DNA replication. a, Exonuclease activity of\nphi29DNAP. In the absence of initiation primers and dNTPs, ssM13 DNA was completely\ndegraded within 2 h (reaction setup described in Methods). Fold changes were determined by\nqPCR relative to the ssM13 input level. b, Effect of replication initiation complex concentration.\nDNA replication efficiency was strongly dependent on the concentration of the replication\ninitiation complex. Fold changes were determined by qPCR relative to the DNA content of the\ncontrol. c, Effect of pyrophosphate (PPi) on net DNA synthesis. Fold changes were determined\nby qPCR relative to the DNA content of ssM13 replication reaction without added PPi (0 mM).\nData are presented as means, with error bars indicating the standard deviation from three\nindependent experiments (n = 3).\nCalculation of net DNA synthesis activity using the following formula:\nNet DNA synthesis activity\nDNA content in the polymerization reaction− DNA content in the degradation reaction\n=\ninitial DNA content×reacton time (1 h)\nWhere:\n“DNA content in the polymerization reaction” is qPCR quantification data of ssM13 replication\nreaction with different PPi concentrations in the reaction buffer;\n“DNA content in the degradation reaction” is qPCR quantification data of ssM13 content with\ndifferent PPi concentrations in the reaction buffer without adding initiation primers and dNTPs.\n6.2. In the sentence on page 4, “We then replaced the cssDNA…”, the author wrote\n“successful replication of pJL1-T7-sfGFP,” but replication was just 1.3 fold. How did\nthe authors conclude that this was successful?\nResponse: Thank you for this valuable comment. Our use of “successful replication”\nrefers to the demonstrable ability of the LoopReX system to initiate and carry out DNA\nsynthesis from a circular double-stranded plasmid (pJL1-T7-sfGFP) under lysate-\nbased cell-free conditions. Although the fold increase was modest (1.3-fold), it was\nconsistently above background and reproducible across multiple experimental\nreplicates, indicating the DNA amplification activity by phi29DNAP in the presence of\ncircular double-stranded plasmid.\nTo address this concern, we have added the following text in the revised manuscript\nto emphasize that the observed replication was modest but reproducible, representing\na foundational step toward developing more robust DNA replication in lysate-based\ncell-free systems.\nSpecifically, we now add the text as follows (Page 4):\n“This modest (1.3-fold, Fig. 1b) but reproducible increase in DNA quantity indicates\nthat the LoopReX system supports limited DNA replication of circular plasmids under\ncurrent conditions, establishing a foundation for further optimization.”\n6.3. In the sentence in page 4, “However, the DNA content in …”, the author wrote\n“with DNA replication supporting the template synthesis continuously.” I did not\nunderstand the logic of the author. Certainly, the fold changes were higher for LoopRex\nsystem at all points in Fig 1c, but this can be because DNA replicated for LoopRex\nsystem before 0.5 h and then DNA might stop replication and just degraded afterward.\nIn this case, template synthesis (i.e., DNA replication) did not proceed continuously.\nResponse: Thank you for your insightful comment. As you correctly point out, while the\nLoopReX system showed higher DNA content than the control at all measured time\npoints in Fig. 1c, this does not necessarily prove continuous replication over time, and\nwe cannot rule out the possibility that DNA replication occurred predominantly during\nan early time window (e.g., before 0.5 h), followed by degradation.\nTo make this clear and avoid misinterpretation, we have deleted “with DNA replication\nsupporting the template synthesis continuously” and revised the related text as follows\n(Page 4):\n“Specifically, DNA content remained high during the first hour and then gradually\ndecreased over the next five hours (Fig. 1c), likely due to nuclease-mediated\ndegradation in the cell lysate. Nonetheless, LoopReX consistently maintained higher\nDNA levels than the control CFE system at all time points.”\n6.4. The last sentence on page 5 contains the word “random,” which probably mentions\nthe nucleic acids present in the cell extract. I think that nucleic acids should be rRNA,\ntRNA, mRNA, and fragments from the genome. They are never random. Please\nrephrase it. More importantly, I could not understand the conclusion of Fig. S4,\n“the nucleic acids present in the cell extract still effectively supported the synthesis of new\nDNA strand.” To draw this conclusion, the author would need DNA replication\nexperiment with or without the nucleic acid in the cell extract, but there is no such data.\nInitially, I thought Fig. S4C was the data, but the author wrote in the legend that the\nprimer used there was a specific primer, not the nucleic acids in the extract.\nAccordingly, I could not agree with the first sentence on page 6, “Our findings indicate\nthat ….” Overall, this manuscript lacks explanation of the data, especially in the\nsupplementary figures. I strongly recommend that the authors provide a more detailed\nexplanation of the Supplementary Figures.\nResponse: Thank you for the thoughtful and constructive comments. We agree that\nthe term “random” was imprecise in describing endogenous nucleic acids in the cell\nextract. In the revised manuscript, we have replaced it with “diverse” to provide a more\naccurate description.\nRegarding our interpretation of Fig. S4 (now renumbered as Supplementary Figure 5),\nwe appreciate your critical evaluation and acknowledge that our original conclusion\nwas not sufficiently clear. In the original Fig. S4C, we compared ssM13 replication in\nLoopReX reactions with (+) or without (–) the addition of specific primers. Notably,\nreplication still occurred in the absence of added primers (the “–” group), suggesting\nthat endogenous nucleic acids present in the extract could act as primers for DNA\nreplication. Based on this, we concluded that “the nucleic acids present in the cell\nextract still effectively supported the synthesis of new DNA strands.”\nTo strengthen this conclusion, we performed additional validation experiments as you\nsuggested. Specifically, we purified endogenous nucleic acids from the cell extract and\nadded them to a defined, pure system-based DNA replication reaction containing\nphi29DNAP and ssM13 template. Consistent with our hypothesis, purified endogenous\nnucleic acids supported DNA replication, whereas no replication occurred in their\nabsence. Moreover, increasing the amount of nucleic acids enhanced DNA synthesis\n(new data are provided in Figure R2). These results are now included in the updated\nSupplementary Figure 5 (original Fig. S4), along with a revised figure legend for clarity\n(please see Supplementary Information for more details). We hope that these new\nexperiments/data, together with the clarified explanation, adequately address your\nconcern and provide stronger support for our conclusion.\nFigure R2. Replication of ssM13 in a defined (pure) reaction system. DNA replication\nreactions were performed using phi29DNAP, ssM13 template, and purified endogenous nucleic\nacids (background nucleic acids, BNAs). The results demonstrate that BNAs isolated from the\ncell extract can function as primers to initiate DNA replication. No replication was observed in\nthe absence of BNAs, whereas increasing BNA concentrations supported higher levels of DNA\nsynthesis. Data are presented as means, with error bars indicating the standard deviation from\nthree independent experiments (n = 3).\n6.5. Page 8. What is ZGBoost models. A brief explanation is needed in the main text.\nResponse: Thank you for raising this point of clarification. We have now added a brief\nexplanation of XGBoost (eXtreme Gradient Boosting) and cited the original publication\ndescribing XGBoost (Chen and Guestrin, 2016 – see Ref. 45) in the revised manuscript\nas follows (Page 9):\n“For in silico optimization, we employed XGBoost (eXtreme Gradient Boosting)45, a\ndecision tree-based ensemble method known for strong predictive performance,\nscalability, and native handling of missing values and complex feature interactions.\nMoreover, XGBoost mitigates overfitting and improves model generalization through\nregularization, learning-rate shrinkage, subsampling of rows and columns, and early\nstopping.”\nRef. 45: Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. KDD’16:\nIn Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery\nand Data Mining, 785-794 (2016).\n6.6. Machine learning part. The explanation of the dataset used for machine learning\nwas insufficient. Please explain how the author got the data. Which factors changed?\nHow much did they change? How much data did they get?\nResponse: Thank you for these helpful comments. We have expanded the manuscript\nto clarify how the data were generated, which factors were varied, and how the dataset\nwas constructed. The following text has been added to the revised manuscript (Page\n10):\n“We evaluated 16 factors (see “Factor selection and data processing” in Methods for\ndetails), and all experimental data were generated in our laboratory. For model\ndevelopment, the datasets comprised 115 experimental conditions for DNA replication\nand 126 experimental conditions for protein expression, each representing a distinct\ncombination of reactants and their concentrations in the LoopReX system. Across\nsamples, the varying features were the identity and concentration of input reactants,\nwhile all other experimental parameters were kept constant. The collected data were\nrandomly split, with 80% used for model training and 20% reserved for testing, and\nfive-fold cross-validation was performed to assess and enhance the robustness of our\nmodels.”\n7. Page 6. About the sentence, “Importantly, we also observed a remarkable increase\nin protein express level… “, why did the protein expression increase? I would like to\nknow the author’s opinion.\nResponse: We appreciate your question and share the interest in understanding the\nmechanism. We believe the increase in protein expression is primarily attributed to the\nposition and sequence features of the artificial origin within the plasmid (Fig. 1i, left).\nThis artificial origin, which is derived from the 3*φ10 promoter, appears to function as\nan additional promoter recognized by T7 RNA polymerase. As a result, it drives the\ntranscription of more mRNA, thereby leading to higher protein expression compared\nto the 3*φ10 promoter alone (Fig. 1i).\nTo test this hypothesis, we performed additional experiments during the revision (see\nFigure R3, provided below for review only). Specifically, we compared mRNA levels\ngenerated from plasmids containing either the 3*φ10 promoter or the artificial origin\n(Arti-ori). After in vitro transcription, mRNA from both groups was converted into cDNA.\nAccording to our model (Figure R3a), the 3*φ10 plasmid group produces only one type\nof cDNA (cDNA-1), while the artificial origin-based plasmid (Arti-ori) can produce two\ntypes (cDNA-1 and cDNA-2) due to the presence of a second transcription start site.\nTo distinguish these transcripts, we designed two primer pairs targeting different\nregions in the cDNA. Primer-1 amplifies the sfGFP gene present in cDNA-1, whereas\nprimer-2 specifically amplifies the region between the artificial origin and the canonical\npromoter in cDNA-2 (Figure R3a). We detected more primer-1 product from the Arti-\nori plasmid than from the 3*φ10 plasmid, and primer-2 products were observed only\nwith the Arti-ori plasmid. These results support our hypothesis that the artificial origin\nenhances transcriptional output, which explains the increased protein expression.\nWhile we cannot fully exclude additional contributions (e.g., mRNA stability or plasmid\narchitecture), our results point to transcriptional upregulation via the artificial origin\nacting as an auxiliary T7 promoter as the primary cause of the increased protein yield.\nPlease note that Figure R3 is provided for your reference and is not included in the\nmain text or Supplementary Information.\nFigure R3. Transcript analysis of the artificial origin-based plasmid (Arti-ori). a, Workflow\nfor transcript analysis via in vitro transcription, reverse transcription, and qPCR using two primer\npairs. b, Comparison of transcript levels generated from plasmids containing either the 3*φ10\npromoter or the artificial origin (Arti-ori). Fold changes were determined by qPCR relative to the\nreference 3*φ10-based plasmid. Data are presented as means, with error bars indicating the\nstandard deviation from three independent experiments (n = 3).\n8. The replication of 3*phi10 was greater than 3, as shown in Fig. 1h, but 1 in Fig. 1i.\nWhy?\nResponse: Thank you for pointing out this source of confusion. In Fig. 1i, DNA\nreplication efficiencies of plasmids with different artificial origin positions were\ncompared after normalization to the control plasmid (3*φ10). For this reason, the\nreplication value of 3*φ10 appears as 1 in Fig. 1i, whereas in Fig. 1h the absolute fold\nchange is shown. We agree that the original y-axis label in Fig. 1i (“fold change”) was\nmisleading. To improve clarity, we have revised the label to “pJL1-T7-sfGFP replication\n(normalized to 3*φ10).” Please see the updated Fig. 1i in the revised manuscript.\n9. As shown in Figs. 2g and 2h, the improvements in LoopRex-Opt are seemingly small.\nIs this unexpected result or reasonable? I’d like to know the author’s opinion.\nResponse: Thank you for this insightful observation. We agree that the improvements\nin DNA replication and protein expression in LoopReX-Opt (Figs. 2g and 2h) are\nmoderate rather than dramatic. However, we believe these results are reasonable and\nmeaningful. In our machine learning-based optimization, two independent models\nwere developed to predict DNA replication and protein expression, respectively. The\neffects of the selected formulations (F1-F5) on these two outputs did not always align.\nFor example, F5 yielded the lowest DNA replication efficiency but the second-highest\nprotein expression level (Figs. 2c and 2d). As a compromise, we selected formulation\nF3 as the optimal condition (LoopReX-Opt) for balancing coupled DNA replication and\nprotein expression.\nAlthough the overall improvements are moderate (Figs. 2g and 2h), LoopReX-Opt\nconsistently outperformed LoopReX-Ori. Importantly, protein expression efficiency\nwas notably enhanced: LoopReX-Opt reached maximum protein yield after only 3\nhours (Fig. 2h), nearly 2-fold higher than LoopReX-Ori at the same time point. This\nacceleration is meaningful as it enables LoopReX-Opt to serve as a more efficient\nplatform for producing target molecules, such as cellular components for artificial cell\nconstruction.\nTo clarify this point, we have added the following text to the revised manuscript (Page\n12):\n“Although the improvements in DNA replication and protein expression were moderate\n(Fig. 2g,h), these results reflect the complex interplay of reaction components in\nLoopReX and demonstrate the utility of machine learning for optimizing such\nmultifactorial systems. Importantly, LoopReX-Opt accelerates genetic information\ntransfer and provides a more efficient TTcDR framework, which could be valuable for\nadvancing artificial life research.”\n10. Fig. 4f. The protein expression yield for Unmodified IdsDNA was much lower than\nprevious data (e.g., Fig. 2h). Why?\nResponse: Thank you for this important observation. The low protein expression yield\nobserved for “Unmodified ldsDNA” (Fig. 4f) is attributable to the low amount of DNA\ntemplate retained in the reaction. During nucleoid preparation, DNA templates were\nincubated with artificial nucleoids and then washed three times to remove unbound\nDNA (see Methods, “Preparation and recovery of artificial nucleoids”). Because\nunmodified ldsDNA lacks biotin, it could not be efficiently anchored to the nucleoids,\nleaving only small amounts of nonspecifically bound DNA available for protein\nexpression. This explains the markedly lower yield observed in Fig. 4f.\nTo confirm this, we conducted additional experiments to measure binding efficiencies\nof different ldsDNA templates. We found that all three types of biotin-modified ldsDNA\ntemplates exhibited nearly 100% binding efficiency, whereas unmodified ldsDNA\ndisplayed substantially lower binding, consistent with nonspecific interactions (see\nbelow Figure R4). These results have now been included in the Supplementary\nInformation (Supplementary Figure 13).\nIn addition, we have added the following clarification to the revised manuscript (Pages\n16-17):\n“We first evaluated the binding efficiency of DNA templates to artificial nucleoids and\nfound that all biotin-modified templates bound with nearly 100% efficiency, whereas\nunmodified ldsDNA showed markedly lower binding efficiency, likely due to nonspecific\ninteractions (Supplementary Fig. 13).”\n100\n50\nN N N N\nD D D D\ns s s s\nd d d d\nd\nl\nn\nl\nn\nl\nn\nl\ndifi\ne\nbi\noti\nbi\noti\nbi\noti\nm\no F- R- al-\nU n D u\nFigure R4. Binding efficiency of different ldsDNA templates to artificial nucleoids.\nArtificial nucleoids were incubated with either unmodified ldsDNA or biotin-modified ldsDNA\ntemplates, followed by three washing steps to remove unbound DNA. The retained DNA was\nquantified to determine binding efficiency. Results show that all three types of biotin-modified\nldsDNA templates exhibited nearly 100% binding efficiency, whereas unmodified ldsDNA\ndisplayed significantly lower binding, consistent with nonspecific interactions. Data are\npresented as means, with error bars indicating the standard deviation from three independent\nexperiments (n = 3).\nMinor points\n1. Figure S1. How to stain DNA?\nResponse: Thank you for pointing this out. DNA was stained using YeaRed Nucleic\nAcid Gel Stain (Yeasen, Shanghai, China). We have added this information to the\nrelevant figure legends (Supplementary Figures 1-3) as well as to the Methods section\nunder “Gel analysis of DNA replication products”.\n2. Figure S2 How to measure ssM13 replication?\nResponse: Thank you for pointing this out. We have added this information to the\nlegend of Supplementary Figure 2 as follows:\n“ssM13 replication was determined by qPCR relative to the ssM13 input level.”\n)%(\nycneiciffe\ngnidniB\nReviewer #2 (Remarks to the Author):\nThis article presents a thorough proof of concept that a minimal circuit for DNA\nreplication can be designed and iterated upon in E. coli lysate. Trying to expand cell\nfree to beyond just in vitro transcription and translation to extend to replication is\nconceptually useful, and this is a thoroughly completed proof of concept involving\nmachine learning, demonstration of improved DNA replication, and\ncompartmentalization, and demonstration that it can improve the concentration of\nreporter proteins.\nA few comments, that are mostly high level, but I think a large re-write adjusting the\nframing is necessary for publication.\nResponse: We sincerely thank you for evaluating our manuscript and for your valuable\ncomments and positive assessment of the concept and strengths of our work. We\ngreatly appreciate your recognition of our efforts to extend cell-free systems beyond\ntranscription and translation by incorporating a minimal DNA replication circuit. We\nhave carefully considered your suggestions and have addressed your comments point-\nby-point below.\n1. Framing it as “artificial central dogma” complete obfuscates the entire premise of\nthe article. Canonically, the central dogma refers to the flow of information in cells\n(DNA-->RNA-->Proteins) which certainly happens in any lysate or PURExpress-based\nTXTL system wherein transcription and translation are necessary to transfer\ninformation. Replication is important for cells, but I don’t think this is generally what\npeople think of when they consider the central dogma of biology. For this reason I think\n“reconstituting artificial central dogma” feels confusing and somewhat jargon-y and\nmakes it harder for the reader to digest this information. I think a concept of the central\ndogma that includes regeneration and self-replication has come into dialogue in part\nof the synthetic biology/origins of life/artificial cells community, but it is not widely used\nlanguage in the broader synthetic biology community, much other areas of molecular\nbiology.\nI recommend that throughout the manuscript, “artificial central dogma” is replaced with\nmore precise language. I also recommend a title change.\nResponse: Thank you for your thoughtful and constructive feedback. We fully agree\nthat the canonical definition of the central dogma refers to the flow of information from\nDNA to RNA to proteins. We understand that framing our study using the term “artificial\ncentral dogma” may lead to confusion, especially as this terminology is not widely used\nacross the broader molecular biology and synthetic biology communities.\nTo improve clarity, we have replaced all instances of “artificial central dogma” in the\nmanuscript with more accurate and specific phrases such as “transcription–translation-\ncoupled DNA replication (TTcDR)”. In line with your recommendation, we have also\nupdated the manuscript title to “Reconstituting transcription–translation-coupled DNA\nreplication within complex in vitro biological systems” to improve the accessibility for a\nbroader readership.\n2. Related—on page 2 there is a discussion of the PURExpress system and its\nlimitations:\n“Elements)-based cell-free expression system, which operates within a well-defined\n(bio)chemical environment6, has been widely used to construct artificial life systems7-\n9. However, these systems often exhibit only individual life characteristics, for example,\nself-replication (e.g., DNA replication)7-12, self-evolution13-15, cell division16-18,\ncommunication19-23, or metabolism25. Essentially, PURE system represents a\nrelatively simplified platform for in vitro biology.”\nI may be misunderstanding the point of this sentence but how can PURExpress\nsystems replicate cell division outside of the cell. I believe the point was to say that\nindividual components of life can be replicated, but it is challenging to put it all together,\nbut I would recommend a major rewrite of this section for clarity.\nResponse: Thank you for highlighting this ambiguity in our original text. Our intention\nwas to emphasize that the PURE system has been successfully applied to reconstitute\nspecific, individual life-like processes, but that combining these functions into a unified\nartificial system remains very challenging. We recognize that our original phrasing —\nparticularly references to “self-evolution,” “cell division,” and “communication” — could\nbe misinterpreted as suggesting that the PURE system fully replicates these complex\nprocesses, which was not our intent.\nTo improve clarity, we have revised the text as follows (Page 2):\n“The PURE (Protein synthesis Using Recombinant Elements)-based cell-free\nexpression system, which functions in a well-defined (bio)chemical environment6,\nhas been widely applied to reconstitute specific life-like processes such as DNA\nreplication7-12, gene sequence evolution13-15, cell division-like events16-18, artificial–\nnatural cell communication19-23, and light-driven ATP regeneration24. However, these\nefforts typically address isolated functions, and integrating multiple life characteristics\ninto a single artificial system remains a major challenge given the relatively simplified\nnature of the PURE platform.”\n3. Overall, I caveat the authors on boldly claiming concepts are “confirmed” when they\nare merely supported. An example of this can be found on page 6\n“These findings confirm our hypothesis that the position of the promoters (i.e., the\nartificial origin) plays a crucial role in regulating DNA replication and transcription”\nI think the word “support” would be better than “confirm”. Given the toy system nature\nof this investigation, it is difficult to say how it reacts in more complex situations. It’s\njust one piece of evidence supporting this hypothesis.\nAnother example of this can be found on page 20. Did the authors indicate that\ncompartmentalization is “essential”. I feel this work just shows that it is beneficial and\nimproves protein production, not that this cannot occur without it. The controls\ndemonstrate that this replication can occur, just less efficiently.\nResponse: Thank you for your thoughtful and constructive feedback. We appreciate\nyour suggestions and have revised the relevant sentences to more accurately reflect\nthe scope of our findings and to avoid overstatements.\nSpecifically, we have changed the text on Page 6 to:\n“These findings support our hypothesis that the position of the promoters (i.e., the\nartificial origin) plays a crucial role in regulating DNA replication and transcription.”\nAdditionally, the statement on Page 22 now reads:\n“This spatial compartmentalization is beneficial for efficient DNA replication...”\n4. In Supplementary Figure 10 and Figure 4c could use a clearer explanation of why\nmorphology observed for DNA complexes is lighter and what the TEM is measuring,\nand perhaps a reference for the method—essentially since this is such an\ninterdisciplinary field, it should walk the reader through these experiments.\nResponse: Thank you for this helpful suggestion. We have clarified in the Methods and\nfigure legends how contrast is generated in negative-stain TEM. In bright-field TEM,\ndarker regions correspond to areas enriched with heavy-atom stain (uranyl acetate),\nwhich scatter more electrons, while lighter regions correspond to stain-excluded areas\nthat transmit more electrons. DNA-rich condensates typically exclude uranyl acetate\nfrom their interior, resulting in an electron-lucent (lighter) morphology with darker rims\nat the boundaries where stain accumulates. This appearance is consistent with the\nexpected behavior of nucleic acids and proteins under negative-stain conditions.\nTo make this clear, we have added the following text to the legends of Figure 4c and\nSupplementary Figure 10 and a reference on negative staining (De Carlo and Harris,\nMicron, 2011, 42, 117-131) as follows:\n“Protein–DNA complexes appear lighter under TEM, likely because DNA-rich\ncondensates exclude uranyl acetate stain from their interior, resulting in lower contrast\ncompared to protein alone51.” (Page 18)\nRef. 51: De Carlo, S. & Harris, J. R. Negative staining and cryo-negative staining of\nmacromolecules and viruses for TEM. Micron 42, 117-131 (2011).\nIn addition, we have added a description of the TEM method in the revised manuscript\n(Page 32):\n“Transmission electron microscopy (TEM)\nTEM images were acquired using a JEOL JEM-1400 Plus microscope at an operating\nvoltage of 120 kV. Samples (10 μL of artificial nucleoids resuspended in nuclease-free\nwater) were deposited on a carbon-coated copper grid (300 mesh), excess liquid was\nremoved by filter paper after 1 min, and the grids were air-dried. Negative staining was\nperformed with 2% uranyl acetate (10 μL, 30 s), followed by air-drying before imaging.”\n5. While the machine learning checklist says there are instructions for how to obtain\ncode, I do not see any statement about this written in the text, e.g. if the authors intend\nto deposit Juptyr notebooks on GitHub.\nResponse: Thank you for bringing this to our attention. We apologize for the omission.\nIn the revised manuscript, we have included the “Code availability” section as follows\n(Page 33). We also updated the machine learning checklist for your reference.\n“Code availability\nAll codes used for model development and analysis have been deposited in GitHub\nand can be accessed at https://github.com/BMG-FDU/XGBoost_model.git.”\nReviewer #3 (Remarks to the Author):\nThe manuscript “Reconstituting artificial central dogma within complex in vitro\nbiological systems” demonstrated the central law in a complex in vitro system for the\nfirst time, which provides a new idea for the construction of artificial life system. I still\nhave some comments that should be addressed prior to publication of this manuscript.\nThe points would need to be addressed as follows.\nResponse: We thank you for thoroughly evaluating our manuscript and highlighting the\nvalue and novelty of our study. We greatly appreciate your constructive comments and\nhave carefully considered and addressed each of your concerns point-by-point below.\n1. Although the study revealed the synergistic effect of T7RNAP and phi29DNAP in\nDNA replication, the specific molecular mechanism remains to be further explored. It\nis suggested that biochemical analysis or structural biology methods should be used\nto further elucidate the specific roles of these enzymes in the replication process. For\nexample, ‘forming a replication initiation complex for DNA replication’ (Fig.1g), the\ncomplex should be considered.\nResponse: Thank you for your insightful suggestions. We agree that further elucidation\nof the molecular mechanisms underlying the synergistic effect between T7RNAP and\nphi29DNAP, particularly regarding the formation of a replication initiation complex,\nwould significantly enhance our understanding.\nIn our current study, we specifically examined how and where primers are generated\nto initiate replication. Previous studies have shown that T7RNAP can produce different\nRNA products depending on the promoter, including full-length mRNA and abortive\ntranscripts (typically 3-8 nucleotides). To investigate this, we constructed plasmids with\ndifferent promoters and observed that T7RNAP and specific promoters indeed\nfacilitated replication initiation, although the efficiency varied among different\npromoters (Fig. 1f). In addition, to address whether endogenous nucleic acids from the\ncell extract could serve as primers, we performed new experiments during the revision.\nWe purified endogenous nucleic acids and added them to a defined in vitro DNA\nreplication system containing phi29DNAP and an ssM13 template. We found that\nthese nucleic acids supported DNA replication, and increasing their concentration\nenhanced DNA synthesis (see Figure R5 below). Without endogenous nucleic acids,\nwe observed no replication. These results are now included in Supplementary Figure\n5 (original Fig. S4).\nFigure R5. Replication of ssM13 in a defined (pure) reaction system. DNA replication\nreactions were performed using phi29DNAP, ssM13 template, and purified endogenous nucleic\nacids (background nucleic acids, BNAs). The results demonstrate that BNAs isolated from the\ncell extract can function as primers to initiate DNA replication. No replication was observed in\nthe absence of BNAs, whereas increasing BNA concentrations supported higher levels of DNA\nsynthesis. Data are presented as means, with error bars indicating the standard deviation from\nthree independent experiments (n = 3).\nAdditionally, we have conducted further biochemical assays to determine whether\nthere is a direct interaction between T7RNAP and phi29DNAP in our reaction system.\nWe first performed a pull-down assay by purifying phi29DNAP from the LoopReX\nreaction. SDS-PAGE analysis revealed no T7RNAP band in the pull-down sample,\nindicating the absence of a stable T7RNAP–phi29DNAP complex (see Figure R6\nbelow). We then carried out isothermal titration calorimetry (ITC) by titrating\nphi29DNAP into T7RNAP, and again found no detectable binding affinity (see Figure\nR7 below). These results suggest that T7RNAP and phi29DNAP do not directly interact\nin our system. Please note that Figures R6 and R7 are provided for your reference and\nare not included in the main text or Supplementary Information.\nFigure R6. SDS-PAGE analysis of T7RNAP and phi29DNAP following the pull-down\nassay. In the “Pull down” lane, only the protein band corresponding to phi29DNAP is visible.\nThe “Standard” lanes show the purified T7RNAP and phi29DNAP, respectively.\nFigure R7. ITC assay of the interaction between T7RNAP and phi29DNAP. phi29DNAP (50\nμM) was titrated into T7RNAP (5 μM), and no heat release was detected, which indicates no\ninteraction between phi29DNAP and T7RNAP.\nRegarding the phrase “forming a replication initiation complex for DNA replication (Fig.\n1g),” we agree that, as we have no direct experimental evidence, it may be misleading.\nTherefore, we have removed this statement from the revised manuscript (Page 5).\n2. The current system is mainly focused on DNA replication and protein expression,\nand more cellular functions such as metabolic pathways and signal transduction can\nbe considered.\nResponse: Thank you for this valuable suggestion. We share your interest in extending\nthe system to incorporate additional cellular functions. In our original submission (Fig.\n5b-d), we demonstrated that artificial nucleoids functionalized with the enzyme LacZ\ncould catalyze the conversion of o-nitrophenyl-β-D-galactopyranoside (ONPG) into o-\nnitrophenol (ONP). Following your suggestion, we performed additional experiments\nto reconstitute a two-enzyme metabolic pathway for the biotransformation of styrene\ninto (S)-1-phenyl-1,2-ethanediol. Specifically, two enzymes, StyA and SpEH, were co-\nexpressed and self-assembled into artificial nucleoids, enabling efficient conversion of\nstyrene to the desired product. These results are now included in Supplementary\nFigure 14 (see Figure R8 below).\nWe have also added the following text to the revised manuscript (Page 19):\n“Additionally, we successfully constructed a two-enzyme metabolic pathway by co-\nexpressing StyA (the oxygenase subunit of styrene monooxygenase) and SpEH\n(epoxide hydrolase)27 with CipB-streptavidin, which self-assembled into artificial\nnucleoids and catalyzed the conversion of styrene into (S)-1-phenyl-1,2-ethanediol\n(Supplementary Fig. 14).”\nAs our main objective was to reconstitute DNA replication and protein expression in a\ncomplex in vitro system, we did not extend our study to include artificial cell\nconstruction or signal transduction in this work. Nonetheless, we agree that expanding\nLoopReX to support such higher-order cellular functions is an exciting direction, and\nwe plan to pursue this in future studies.\nFigure R8. Reconstitution of a two-enzyme metabolic pathway in LoopReX. a, Schematic\nof the pathway for conversion of styrene to (S)-1-phenyl-1,2-ethanediol. StyA, the oxygenase\nsubunit of styrene monooxygenase; SpEH, epoxide hydrolase. b, De novo synthesized artificial\nnucleoids incorporating StyA and SpEH enzymes in LoopReX. c, High-performance liquid\nchromatography (HPLC) analysis of standard (top), negative control reaction without DNA\ntemplates (middle), and reaction sample (bottom).\n3. Besides being faster, what other advantages does LoopReX have? Where does\nmore DNA than CFE flow? (Fig.1d)\nResponse: Thank you for this thoughtful comment. In Figs. 1c and 1d, we compared\nDNA replication and protein expression between the initial LoopReX system and a\ntraditional CFE system. We observed that LoopReX consistently outperformed CFE in\nboth aspects. Specifically, DNA replication in LoopReX was about two-fold higher than\nin CFE (Fig. 1c), providing the foundation and motivation for further optimization. In\naddition, protein expression in LoopReX proceeded more rapidly, reaching its peak at\nleast one hour earlier than in CFE within the 6-hour window (Fig. 1d). After machine\nlearning-guided optimization, the enhanced LoopReX-Opt system achieved maximum\nprotein yield within just 3 hours, which is substantially faster than the original LoopReX\n(8 hours; Fig. 2h) and CFE (14 hours; Supplementary Fig. 9). We believe this\naccelerated protein expression is a key advantage of LoopReX, particularly for\napplications requiring efficiency and high throughput.\nRegarding more DNA levels in LoopReX, we note that DNA content still decreased\nafter the first hour (Fig. 1c), indicating that DNA degradation occurs in both systems.\nHowever, the transiently elevated DNA levels in LoopReX likely contributed to the\nfaster protein expression observed in Fig. 1d.\nTo clarify this in the manuscript, we have revised the text as follows (Page 5):\n“Additionally, protein expression in LoopReX reached its maximum earlier than in the\ncontrol CFE system, suggesting a faster expression rate that offers advantages in\nefficiency and throughput. Taken together, these findings demonstrate that LoopReX\noutperforms the CFE system in both DNA replication and protein expression, providing\na robust platform for subsequent optimization.”\n4. In machine learning, is the division of the training set and the test set random? What\nis the basis of the division?\nResponse: Thank you for the helpful comment. Yes, the division of the training (80%)\nand test (20%) sets was performed randomly to ensure that both are representative of\nthe overall data distribution and to minimize selection bias. To further improve\nreproducibility and statistical robustness, we employed five-fold cross-validation during\nmodel development. All random splits were conducted with a fixed random seed to\nmaintain consistency across repeated experiments.\nTo clarify this point, we have added the following text to the revised manuscript (Page\n10):\n“We evaluated 16 factors (see “Factor selection and data processing” in Methods for\ndetails), and all experimental data were generated in our laboratory. For model\ndevelopment, the datasets comprised 115 experimental conditions for DNA replication\nand 126 experimental conditions for protein expression, each representing a distinct\ncombination of reactants and their concentrations in the LoopReX system. Across\nsamples, the varying features were the identity and concentration of input reactants,\nwhile all other experimental parameters were kept constant. The collected data were\nrandomly split, with 80% used for model training and 20% reserved for testing, and\nfive-fold cross-validation was performed to assess and enhance the robustness of our\nmodels.”\n5. In Figure 3b, the increase of DNA mass is due to the continuous enlargement of the\nsystem or the continuous iteration of culture?\nResponse: Thank you for this question. As shown in Fig. 3a, continuous iteration\nthrough serial dilutions enabled sustained DNA amplification. Building on this, in Fig.\n3b we performed successive saturated iterative reactions, where the entire reaction\nmixture from the previous round was used as a seed to inoculate fresh reactions at a\nfixed 1:3 ratio (seed:fresh mix). Thus, the observed increase in DNA mass in Fig. 3b\nreflects continuous iteration of the culture, with replicated DNA serving as templates\nfor protein expression. We then calculated the cumulative theoretical protein yields\nacross iterative rounds (Fig. 3b, right).\nTo make this clear, we have added the following text to the legend of Fig. 3b (Page\n15):\n“Seed DNA from the previous round was carried into the next round (1:3 ratio) to\nenable iterative amplification and serve as templates for protein expression.”\n6. In Figure 3c, what are the key differences between in vitro amplification and small\ntrials? Is the effect equal amplification?\nResponse: Thank you for raising this important point. The key differences between in\nvitro amplification and small trials are (i) the reaction scale and (ii) protein yield per\ninitial DNA input. In vitro amplification uses iterative rounds to scale up to larger\nvolumes (e.g., 1 mL), whereas small trials are single-batch reactions (15 µL) without\namplification. Importantly, starting from the same DNA input, protein yields per DNA\ninput were approximately 10-fold higher in in vitro amplification compared to small trials,\ndemonstrating that this approach both conserves DNA and enhances overall product\nyield.\nTo clarify this in the manuscript, we have revised the text as follows (Page 14):\n“Specifically, protein yields per DNA input in (i) single- and (ii) double-amplification\nreactions (up to 1 mL) increased by 9.5- and 15-fold, respectively, compared to the\nstandard 15 µL LoopReX-Opt reaction. This indicates that in vitro amplification not only\nreduces DNA input requirements but also substantially enhances overall product yield.”\n7. Does artificial nucleoid perform other functions, such as signal transduction? What\nare the properties of artificial nucleoid, solid or liquid?\nResponse: Thank you for these insightful questions. As noted in our response to your\ncomment 2, the primary aim of our current study was to reconstitute DNA replication\nand protein expression in a complex in vitro system; we did not extend our investigation\nto artificial cell construction or signal transduction in this work. Nevertheless, we agree\nthat exploring the potential of artificial nucleoids for higher-order cellular functions is a\npromising direction and plan to pursue this in future studies.\nTo demonstrate additional functionality of artificial nucleoids, during the revision we\nconducted new experiments reconstituting a two-enzyme metabolic pathway by co-\nexpressing StyA (the oxygenase subunit of styrene monooxygenase) and SpEH\n(epoxide hydrolase) with CipB-streptavidin. The resulting artificial nucleoids efficiently\ncatalyzed the conversion of styrene to (S)-1-phenyl-1,2-ethanediol. These findings are\nprovided in Supplementary Figure 14 (please see above Figure R8 and our response\nto comment 2 for details).\nRegarding the properties of artificial nucleoids, CipB (the bacterial protein used in their\nconstruction) is known to form insoluble protein crystalline inclusions. CipB-mediated\naggregation is therefore solid rather than liquid. In our experiments, CipB readily\nformed solid structures ranging from nanoscale to microscale particles (see Fig. 4c\nand 4d), consistent with previous reports (Refs. 48-50). Artificial nucleoids were also\nrecoverable by centrifugation as solid pellets in our study.\nFor clarity, we have revised the text in the manuscript as follows (Page 16):\n“Upon adding DNA, the protein-DNA complex formed similar nano-compartments (Fig.\n4c, right; Supplementary Fig. 11), resulting in artificial nucleoids. These structures\nfurther aggregated into protein-DNA clusters, visible as solid, green particles with\nFAM-labeled DNA (Fig. 4d).”\n8. What is the reason of ‘higher protein concentrations provided enhanced DNA\nprotection’?\nResponse: Thank you for this thoughtful comment. Previous studies, including our own,\nhave shown that linear DNA templates in lysate-based cell-free systems can be\nprotected from nuclease degradation by DNA-binding proteins or immobilization (Zhu\net al., Biotechnol. Bioeng. 2020, 117, 3849-3857; Yim et al, ACS Synth. Biol. 2020, 9,\n2851-2855; Norouzi et al., ACS Synth. Biol. 2021, 10, 1615-1624; Lu et al., ACS\nSustainable Chem. Eng. 2024, 12, 18219-18230). In our system, higher protein\nconcentrations enhanced DNA protection because CipB binds DNA and promotes the\nassembly of artificial nucleoids. At elevated CipB levels, DNA is more effectively\ncoated and compacted, which shields it from nuclease degradation in the cell extract.\nThis protection helps maintain DNA integrity for subsequent amplification and protein\nsynthesis.\nTo clarify this point, we have added the following text to the revised manuscript (Page\n16):\n“This effect is likely due to increased CipB concentrations promoting more efficient\ncoating and compaction of DNA into artificial nucleoids, which in turn shields DNA from\nnuclease degradation in the cell extract, as reported in previous studies36,52-54.”\nRef. 36: Lu, Y. et al. Chitin-functionalized cell-free system enables sustainable\nbiocatalysis and gene expression. ACS Sustainable Chem. Eng. 12, 18219-18230\n(2024).\nRef. 52: Zhu, B. et al. Increasing cell-free gene expression yields from linear templates\nin Escherichia coli and Vibrio natriegens extracts by using DNA-binding proteins.\nBiotechnol. Bioeng. 117, 3849-3857 (2020).\nRef. 53: Yim, S. S., Johns, N. I., Noireaux, V. & Wang, H. H. Protecting linear DNA\ntemplates in cell-free expression systems from diverse bacteria. ACS Synth. Biol. 9,\n2851-2855 (2020).\nRef. 54: Norouzi, M., Panfilov, S. & Pardee, K. High-efficiency protection of linear DNA\nin cell-free extracts from Escherichia coli and Vibrio natriegens. ACS Synth. Biol. 10,\n1615-1624 (2021).\n9. In Figure 4e and 4f, the efficiency of DNA and protein synthesis is not quite the same,\nwhat are the possible reasons?\nResponse: Thank you for this constructive comment. We believe the differences in\nefficiencies observed with DNA replication (Fig. 4e) and protein synthesis (Fig. 4f) arise\nmainly from the binding format of DNA to CipB-assembled scaffolds. Dual-biotin-\nmodified DNA performed best, as both ends were anchored to CipB, which provided\nstrong protection from nuclease degradation and supported efficient template\namplification for protein synthesis. In contrast, single-biotin-modified DNA, with one\nend exposed to nucleases, was more prone to degradation, leading to reduced\nreplication and lower protein synthesis efficiency.\nTo clarify this point, we have added the following text to the revised manuscript (Page\n17):\n“While the overall performance of TTcDR was improved with DNA-loaded artificial\nnucleoids, the efficiencies varied depending on the DNA–CipB binding format. Dual-\nbiotin-modified DNA achieved the highest efficiency, as both ends were protected by\nCipB binding, preventing degradation and enabling more templates for protein\nsynthesis. In contrast, single-biotin-modified DNA, with one free end exposed to\nnucleases, was more readily degraded, resulting in lower DNA replication and protein\nsynthesis efficiency.”\n10. How can it be shown that DNA replication occurs on a nucleoid and transcription\noccurs in liquid?\nResponse: Thank you for this valuable comment. To construct artificial nucleoids, DNA\nwas initially loaded onto CipB-assembled scaffolds and was only detected on these\nnucleoids at the start of the reaction (Fig. 4e, yellow bars at 0 h). After DNA replication,\nthe newly synthesized DNA, lacking biotin-modified ends, could not bind to the CipB\nscaffolds and was released into the supernatant, where it then served as templates for\ntranscription and translation in the liquid phase. Our original text was intended to\nconvey this hypothesized spatial separation. However, as we have not performed\ndirect experiments to confirm this, we have deleted the relevant statement to avoid\nmisinterpretation.\nThe following sentence has been removed from the revised manuscript:\n“The findings also indicate that artificial nucleoids enable spatial separation of DNA\nreplication (which occurs on the nucleoids) and transcription-translation (which occurs\nin the liquid cytoplasm-like environment), thereby facilitating an efficient flow of genetic\ninformation.”\n11. In Figure 5a, how can we undo the effect of non-karyon expression in vivo?\nResponse: Thank you for raising this important point. Since we did not conduct\nexperiments in vivo, we assume your question refers to non-karyon expression in vitro\nas shown in Fig. 5a.\nIn our system, newly replicated, unmodified DNA is released into the liquid phase and\ncan also serve as templates for protein expression. To minimize carryover, after each\nround the hybrid artificial nucleoids (composed of CipB-streptavidin, CipB-mCherry,\nand dual-biotin-modified DNA) are collected, washed, and resuspended for the next\nround of reaction (see “Preparation and recovery of artificial nucleoids” in Methods).\nThis process removes unbound DNA, ensuring that only nucleoid-bound DNA template\nis carried forward to subsequent DNA replication and protein synthesis in the next\nround, thereby reducing non-karyon expression from previous rounds. However,\nduring each new reaction cycle, freshly synthesized DNA in the liquid phase can still\nserve as templates for protein expression and cannot be eliminated during the ongoing\nprocess.\nTo clarify this point, we have added the following text to the legend of Fig. 5a (Page\n20):\n“After each round, hybrid artificial nucleoids composed of CipB-streptavidin, CipB-\nmCherry, and dual-biotin-modified DNA were collected, washed, and resuspended for\nthe next round of reaction (see “Preparation and recovery of artificial nucleoids” in\nMethods). This process removed free unmodified DNA, so that only nucleoid-bound\nDNA templates were carried forward into the next round of DNA replication and protein\nexpression.”\n12. Is it the order of transcription and then replication in vitro? Is the translation process\nsequential? Is there any representation of translation? Is it transcription and translation?\nWhat if it's a eukaryotic system?\nResponse: Thank you for these insightful questions. As proposed in Fig. 1g, T7RNAP\ncan generate two types of RNA products: (i) short abortive transcripts that serve as\nprimers for phi29DNAP-mediated DNA replication, and (ii) full-length mRNAs that\nserve as templates for translation. In this context, transcription can provide primers\nthat initiate replication. However, we also observed that endogenous nucleic acids\npresent in the cell extract could act as primers, enabling phi29DNAP to initiate\nreplication even without transcription-derived RNAs. Meanwhile, full-length mRNAs\nproduced by T7RNAP are translated by the cell extract machinery.\nThus, in our in vitro LoopReX system, replication, transcription, and translation are not\nstrictly sequential but occur in a coupled and partially overlapping manner. We\nemphasize that LoopReX functions as a coupled system rather than a strictly ordered\npathway, with the three processes taking place in parallel and influencing each other.\nLoopReX integrates four core modules: (1) phi29DNAP, (2) T7RNAP, (3) E. coli cell\nextract, and (4) artificial nucleoids, which together coordinate replication, transcription,\nand translation.\nRegarding eukaryotic systems, we agree that a eukaryotic cell extract could, in\nprinciple, be integrated with the other modules to establish a eukaryotic version of\nLoopReX. Exploring such extensions is an exciting future direction that could broaden\nthe applicability of our platform. We have discussed this point in the main text as\nfollows: “Additionally, while LoopReX is based on E. coli crude extracts, future work\ncould explore adapting the system to other cell types or organisms, which would\nrequire further optimization to fully harness the potential of diverse biological\ncomponents.” (Page 23)\n13. What are the advantages of scaling up protein production compared to in vivo, in\nterms of cost and time?\nResponse: Thank you for your thoughtful comment. We agree that it is important to\nconsider the advantages of LoopReX compared to in vivo systems in terms of cost and\ntime. Unlike in vivo systems, where protein expression requires multiple steps\nincluding cell cultivation, genetic transformation, and optimization of culture conditions,\nLoopReX enables direct in vitro protein synthesis from DNA templates. This approach\neliminates the need for time-consuming cell growth and scale-up, allowing proteins to\nbe produced within hours rather than days.\nFurthermore, LoopReX streamlines rapid prototyping and testing of DNA constructs,\nremoving the need for labor-intensive cloning and the maintenance of living cells. This\nsignificantly reduces both the labor and costs associated with strain engineering and\ncell-based workflows. Another key advantage is the iterative amplification capability of\nLoopReX, which lowers the initial DNA template requirements and maximizes protein\nyield per unit of DNA.\nThe cost-effectiveness of LoopReX can be further enhanced by recent developments:\nour group and the Jewett laboratory have employed machine learning- and design-\ndriven strategies to optimize cell-free reagent formulations, substantially reducing the\ncost of cell-free gene expression (Zhu et al., Nat. Commun. 2025, 16, 2720; Olsen et\nal., bioRxiv preprint doi: https://doi.org/10.1101/2025.08.01.668204).\nTaken together, these features confer clear advantages to LoopReX in terms of speed,\nflexibility, and efficiency, especially for rapid prototyping, high-throughput screening,\nand applications where in vivo protein production is hampered by toxicity or metabolic\nburden.\nTo clarify this in the manuscript, we have added the following text (Page 22):\n“Additional advantages of LoopReX include significant time savings and cost-\neffectiveness compared to in vivo protein production. Unlike in vivo systems, LoopReX\nbypasses cloning and cell cultivation, enabling protein synthesis directly from DNA\nwithin hours. Furthermore, when paired with recent machine learning-optimized cell-\nfree formulations that further reduce cost47, LoopReX will provide a rapid, flexible, and\nscalable platform well-suited for prototyping and high-throughput applications.”\n14. What are the intracellular and extracellular aggregation conditions of CipB? Which\nconditions can achieve the best aggregation effect?\nResponse: Thank you for this thoughtful comment. CipB is a bacterial crystalline\ninclusion protein known to self-assemble into scaffolds in various hosts such as\nPhotorhabdus luminescens and E. coli (Refs. 48-50). Previous studies have shown\nthat CipB–enzyme fusion proteins form stable inclusion complexes for efficient\nbiocatalysis. In our current study, CipB was expressed directly in the LoopReX system\nrather than in vivo, and we confirmed successful aggregation under in vitro conditions.\nAlthough we did not systematically compare intracellular versus extracellular assembly,\nboth our data and prior reports support that CipB readily self-assembles across diverse\nin vivo and in vitro environments.\nTo further assess in vitro aggregation, we examined CipB aggregates in PBS buffer\nand pure water during the revision. Atomic force microscopy (AFM) analysis indicated\nthat CipB formed stable aggregates with similar morphologies under both conditions\n(Figure R9). Please note that Figure R9 is provided for your reference only and is not\nincluded in the main text or Supplementary Information.\nFigure R9. AFM analysis of CipB aggregates under PBS buffer and pure water conditions.\nCipB was expressed in LoopReX, purified, and resuspended in different buffers, showing\ncomparable morphologies.\n15. Whether the activity of the enzyme or protein after aggregation with cipB has been\nchanged? Whether the folding of the protein or enzyme has been affected from soluble\nto insoluble? Authors should include some predictive data on the effect of CipB on the\nstructure of the target enzyme or protein, such as using AlphaFold3.\nResponse: We appreciate this insightful comment. To minimize potential interference,\nwe introduced a peptide linker (nine amino acids) between CipB and LacZ in the fusion\nconstruct (Supplementary Table 1). During revision, we directly compared the catalytic\nactivities of LacZ and CipB-LacZ. Both enzymes displayed comparable activities and\nsimilar catalytic profiles (Figure R10), consistent with previous studies showing that\nCipB fusion does not impair enzyme activity (Refs. 48-50). These results suggest that\nCipB assembly does not adversely affect enzyme folding or function.\nIn addition, we used AlphaFold3 to predict the structures of LacZ and CipB-LacZ. The\npredicted LacZ domain in the fusion construct overlapped nearly perfectly with wild-\ntype LacZ (Figure R11), further supporting that CipB does not alter LacZ folding.\nTo clarify these points, we have included Figures R10 and R11 (now Supplementary\nFigs. 26 and 27) in Supplementary Information and added the following text to the\nmanuscript (Page 32):\n“Comparison of enzymatic activities and structural predictions of CipB-LacZ versus\nLacZ are provided in Supplementary Figs. 26 and 27, respectively.”\nXY:Michaelis-Menten (nonlin. reg.)\n6\n4\n2\n0\n0 2 4 6\nONPG (mM)\nFigure R10. Catalytic activities of CipB-LacZ and LacZ. Both enzymes exhibit comparable\nactivities with similar catalytic profiles.\nFigure R11. Structural prediction of CipB-LacZ using AlphaFold3. The LacZ domain (green)\noverlaps closely with wild-type LacZ (cyan), while CipB and the peptide linker (nine amino acids)\nare shown in red and yellow, respectively.\n)¹⁻nim·Mm(\n/\n)V(\netaR\nnoitcaeR\nCipB-LacZ\nLacZ"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors appropriately responded to my comments.", "reviewer_2": "All comments were adequately addressed in this revision", "reviewer_3": "I still have some concern for the revised manuscript.\n1. In the absence of DNA replication, the protein expression efficiency also increased. However, when comparing 2d and 2f, no obvious increase in expression efficiency was observed. What could be the possible reasons for this?\n2. Does the mutual influence between DNA replication and protein expression get resolved? What is the optimal expression method? What does the optimal expression method indicate as the most significant influencing factor? Is this related to the length of DNA primers and the type of expressed proteins?\n3. How is cross-validation specifically applied in the machine learning part? Is it five-fold cross-validation on the entire dataset, and then the average performance of cross-validation is taken as the final result? Or is it first divided into 80/20, and then five-fold cross-validation is performed on the training set?\n4. If the test set contains conditions that are highly similar to those in the training set (for example, different concentrations of the same reactant), the model performance may be overestimated. Although the R² value is high, the ratio of the number of features to the sample size may be too high, which can easily lead to overfitting. Are there better methods for dividing the training set and test set?"}, "author_response": "Point-by-point response to reviewer comments:\nComments in italics\nResponses in blue\nChanges in the body of the manuscript are noted in red.\nReviewer Comments\nReviewer #1 (Remarks to the Author):\nThe authors appropriately responded to my comments.\nResponse: Thank you.\nReviewer #2 (Remarks to the Author):\nAll comments were adequately addressed in this revision.\nResponse: Thank you.\nReviewer #3 (Remarks to the Author):\nI still have some concern for the revised manuscript.\nResponse: We sincerely thank you for your time and effort in reviewing our manuscript.\nYour previous insightful and constructive comments have greatly improved the quality\nand clarity of our work. We appreciate your re-evaluation of the revised version and\naddress your remaining concerns point-by-point below.\n1. In the absence of DNA replication, the protein expression efficiency also increased.\nHowever, when comparing 2d and 2f, no obvious increase in expression efficiency was\nobserved. What could be the possible reasons for this?\nResponse: Thank you for this thoughtful comment. The key difference of Figs. 2d and\n2f is that protein expression in Fig. 2d was coupled with DNA replication, whereas in\nFig. 2f it was not. The data indicate that the overall protein expression efficiency in Fig.\n2f was slightly lower than in Fig. 2d. We believe this is likely because the newly\nreplicated DNA in the LoopReX reactions (Fig. 2d) can also serve as additional\ntemplates for transcription and translation, leading to even higher protein yields\ncompared with reactions lacking DNA replication (Fig. 2f).\nTo clarify this point, we have added the following text to the revised manuscript (Page\n11):\n“However, it is worth noting that the protein expression efficiency without DNA\nreplication (Fig. 2f) was overall lower than that in LoopReX reactions with DNA\nreplication (Fig. 2d). This difference is likely because the newly synthesized DNA in\nLoopReX can also serve as templates for protein expression, thereby enhancing\nprotein yields.”\n2. Does the mutual influence between DNA replication and protein expression get\nresolved? What is the optimal expression method? What does the optimal expression\nmethod indicate as the most significant influencing factor? Is this related to the\nlength of DNA primers and the type of expressed proteins?\nResponse: Thank you for these insightful questions. As reported previously, the mutual\ninterference between DNA replication and protein expression has also been observed\nin the PURE system, which can be partially alleviated by optimizing the Mg²⁺\nconcentration (Seo and Ichihashi, ACS Synth. Biol. 2023, 12, 1813-1822). In our study,\nwe observed a similar phenomenon in LoopReX. Although this interference was not\ncompletely eliminated, we identified a balanced formulation (F3) that represents an\noptimal compromise, achieving the highest protein yield together with a relatively high\nlevel of DNA replication (Fig. 2c,d and Supplementary Fig. 8). Particularly, the Mg²⁺\nconcentration was increased from 12 mM in the original LoopReX to 13.8 mM in the\nF3 formulation, suggesting that a slightly higher Mg²⁺ level helps maintain a balanced\nand efficient system. This observation aligns with the previous report that “maintaining\nan optimal magnesium concentration results in enhanced transcription and translation\nwhile maintaining a certain level of DNA replication in the transcription/translation-\ncoupled DNA replication reaction” (Seo and Ichihashi, ACS Synth. Biol. 2023, 12,\n1813-1822).\nWe also appreciate your questions regarding the potential effects of primer length and\nthe type of expressed proteins on LoopReX performance. To address these points, we\nconducted additional experiments during the revision.\nFirst, we examined the influence of primer length on DNA replication by synthesizing\nrandom primers ranging from 8 to 16 nucleotides in length. To avoid interference from\nendogenous short nucleic acid fragments or T7RNAP-generated abortive transcripts\n(typically 3-8 nt), we tested primer-dependent DNA replication in a defined reaction\ncontaining phi29 DNAP, the ssM13 template, and synthetic primers. The results\nshowed that shorter primers favored DNA replication, with the highest efficiency\nachieved using 8-nt primers, which are similar in length to abortive transcripts (see\nFigure R1 below).\nSecond, we evaluated whether the optimized LoopReX system (LoopReX-Opt) could\nimprove the expression of different protein types beyond the fluorescent reporter\nsfGFP. We selected two representative proteins, which were also used in our study (a\nscaffold protein, CipB, and a functional enzyme, phi29DNAP), and compared their\nexpression in the optimized (LoopReX-Opt) and original (LoopReX-Ori) systems. The\nresults demonstrated that LoopReX-Opt also enhanced the yields of both CipB and\nphi29DNAP relative to the original formulation (see Figure R2 below). These findings\nindicate that the optimized LoopReX system supports robust protein synthesis across\ndifferent protein types.\nWe sincerely appreciate your thoughtful suggestions, which prompted us to further\ninvestigate and deepen our understanding of the LoopReX system. We believe these\nadditional results demonstrate the potential of LoopReX-Opt as a more efficient and\nversatile platform for DNA replication–coupled protein synthesis. (Please note that\nFigures R1 and R2 are provided here for your reference only and are not included in\nthe main text or Supplementary Information.)\n1.5\n1.0\n0.5\n0.0\n8 nt 12 nt16 nt\nPrimer length\nFigure R1. Replication of ssM13 in a defined (pure) reaction system. DNA replication\nreactions were performed using phi29DNAP, ssM13 template, and synthetic random primers\n(8 nt, 12 nt, or 16 nt). Data are presented as means, with error bars indicating the standard\ndeviation from three independent experiments (n = 3).\nFigure R2. Expression of (a) CipB and (b) phi29DNAP in the optimized LoopRex-Opt\nsystem. Both proteins were also expressed in the original LoopReX-Ori system for comparison.\nAfter expression, CipB and phi29DNAP were purified and their yields determined. Data are\npresented as means, with error bars indicating the standard deviation from three independent\nexperiments (n = 3).\n)egnahc\ndlof(\nnoitacilpeR\n31Mss\n500\n400\n300\n200\n100\nRe\nX- O\nL\nr\no\ni\no p\nRe\nX- O pt\n)gμ(\ndleiy\nnoisserpxe\nBpiC\n100\n80\n60\n40\n20\nRe\nX- O\nL\nr\no\ni\no p\nRe\nX- O pt\n)gμ(\ndleiy\nnoisserpxe\nPAND92ihp\na b\nTo make this point clear, we have revised the relevant sentences in the manuscript as\nfollows (Page 10):\n“The collected data were randomly split, with 80% used for model training and 20%\nreserved for independent testing. Then, five-fold cross-validation was performed only\non the training set to assess and enhance the robustness of our models.”\n3. How is cross-validation specifically applied in the machine learning part? Is it five-\nfold cross-validation on the entire dataset, and then the average performance of\ncross-validation is taken as the final result? Or is it first divided into 80/20, and then\nfive-fold cross-validation is performed on the training set?\nResponse: Thank you for raising this point of clarification. In our analysis, we adopted\nthe latter approach. Specifically, the entire dataset was first randomly divided into a\ntraining set (80%) and an independent test set (20%). Five-fold cross-validation was\nthen performed exclusively on the training set. The training set was further divided into\nfive approximately equal-sized subsets. In each iteration, one subset was used for\nvalidation, while the other four subsets were used for model training. This procedure\nwas repeated five times so that each subset served once as the validation set, yielding\nfive sets of performance metrics. The average performance across these folds was\nused for model selection and hyperparameter tuning. After the optimal configuration\nwas determined, the final model was evaluated on the independent test set, and these\nresults were reported as the final performance metrics.\nTo make this point clear, we have revised the relevant sentences in the manuscript as\nfollows (Page 10):\n“The collected data were randomly split, with 80% used for model training and 20%\nreserved for independent testing. Then, five-fold cross-validation was performed only\non the training set to assess and enhance the robustness of our models.”\n4. If the test set contains conditions that are highly similar to those in the training set\n(for example, different concentrations of the same reactant), the model performance\nmay be overestimated. Although the R² value is high, the ratio of the number of\nfeatures to the sample size may be too high, which can easily lead to overfitting. Are\nthere better methods for dividing the training set and test set?\nResponse: Thank you for raising this important comment. We agree with your concern\nand appreciate the opportunity to further clarify the strategies we employed to minimize\nthe risk of model overfitting.\nFirst, to minimize bias, we performed a random split of the entire dataset, allocating\n80% for model training and 20% for independent testing. All test set data points\nremained entirely unseen during training, so model evaluation was conducted\nexclusively on previously unobserved conditions.\nSecond, we employed five-fold cross-validation only within the training set to further\nenhance model robustness and reduce the risk of overfitting. In each iteration, one-\nfifth of the training data was used for validation, and the model parameters were\noptimized based on the averaged performance across all folds.\nThird, the XGBoost algorithm inherently includes several regularization mechanisms,\nincluding L1/L2 penalties, tree pruning, and shrinkage, which help to constrain model\ncomplexity and effectively prevent overfitting during the boosting process. Specifically,\nnew regression trees are added sequentially to minimize residuals while existing trees\nremain fixed, ensuring that each iteration contributes only incremental improvements.\nTogether, these approaches (i.e., randomized data splitting, rigorous cross-validation,\nand algorithmic regularization) enabled us to minimize the risk of overfitting and ensure\nrobust model generalization. Consistent with this, comparative performance metrics\n(training R² = 0.99 vs. test R² = 0.81 and 0.88) suggest that our models do not exhibit\nsignificant overfitting in practice.\nWe are grateful for your insightful feedback and hope this clarification addresses your\nconcerns."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors have fully addressed all of the concerns raised."}, "author_response": ""}]

Decision letter