Week 1 HW: Final Project Ideas

Automated Biomanufacturing of Tissue-Mimicking Phantoms for Electrosurgical Device Testing

1. What do you want to make and why?

I want to develop a multilayer tissue-mimicking phantom for testing and optimizing electrosurgical devices.

The goal is to create a standardized material that reproduces selected electrical, mechanical, and thermal properties of soft biological tissues. This would allow electrosurgical experiments to be performed under more controlled and reproducible conditions.

My motivation comes from my experience developing a closed-loop electrosurgical system. During electrosurgical experiments, biological tissue can introduce variability between samples, making it difficult to compare device parameters, control strategies, and tissue responses. A standardized tissue-mimicking material could provide a more reproducible platform for evaluating electrosurgical power delivery, temperature response, cutting, and coagulation.

I am particularly interested in developing a multilayer phantom inspired by breast tissue, with different regions representing skin, adipose tissue, and breast tissue. In a later version, a region with different material properties could be incorporated to represent a tumor or lesion.

Previous research has demonstrated that tissue-mimicking phantoms can be engineered with tunable electrical, mechanical, and thermal properties. Gellan-gum-based phantoms, for example, have been developed by modifying their composition to obtain properties that approximate biological tissues [1]. More recently, conductive hydrogel and ionogel phantoms have been developed specifically for electrosurgical training and testing, including multilayer models designed to reproduce tissue stratification [2].

My long-term goal is to create a reproducible material platform that can reduce dependence on biological tissue for repeated electrosurgical testing and support the optimization of electrosurgical devices and control strategies.

2. What living system would make it?

I would use a microbial production system (specifically the bacterium Sphingomonas elodea) to produce a polysaccharide-based biomaterial, with gellan gum as an initial candidate. What goes in: A liquid culture medium containing a carbon source (such as glucose), a nitrogen source, and inorganic salts.

The biological process: The S. elodea will undergo aerobic fermentation in a bioreactor. As the bacteria metabolize the glucose, they synthesize and secrete a high-molecular-weight exopolysaccharide (gellan) into the surrounding broth as a protective mechanism.

What comes out: A highly viscous fermentation broth. To isolate the target product, the bacteria are removed, and the pure gellan gum is precipitated out of the broth using an alcohol like isopropanol, yielding a dry biopolymer powder. This powder is then rehydrated with specific ionic crosslinkers to form the tissue phantoms.

Gellan gum is a microbial polysaccharide that can form hydrogels with tunable mechanical and electrical properties. Previous studies have investigated gellan-gum-based tissue phantoms as materials for reproducing relevant properties of biological tissues [1,3].

3. How would automation help?

Automation could be used throughout the manufacturing and testing workflow.

First, automated liquid handling could prepare many different biomaterial formulations by varying parameters such as polymer concentration, ionic content, water content, and crosslinking conditions. I would use a desktop liquid-handling robot, such as an Opentrons OT-2, to automate the high-throughput preparation of hundreds of candidate biomaterial formulations, precisely varying the gellan concentration, water content, and ionic crosslinkers to tune the electrical and thermal properties.

Second, automated dispensing or additive manufacturing could fabricate the phantom layer by layer, allowing different compositions to be assigned to different tissue regions. Once a formulation is optimized, I would use an automated robotic bioprinter (such as a CELLINK dual-printhead system) to extrude the phantom layer-by-layer. This allows different anatomical compositions (e.g., highly conductive glandular tissue vs. highly resistive adipose tissue) to be precisely assigned to specific spatial regions, mimicking the stratification of real breast tissue.

Third, automated characterization could measure relevant electrical, mechanical, and thermal properties of each formulation.

Finally, these measurements could be incorporated into a feedback loop to select and refine formulations:

Design → Manufacture → Measure → Compare → Adjust → Manufacture again

The electrosurgical testing stage could also be automated. The phantom could be exposed to controlled electrosurgical conditions while sensors measure variables such as electrical response and temperature. These measurements could then be used to evaluate whether the phantom reproduces the desired tissue-like response.

This closed-loop approach would combine biomanufacturing, automation, materials characterization, and control systems.

4. How much and how long?

(a) How much? To define a meaningful scale, I am estimating the production of 1,000 multilayer breast phantoms to supply a large-scale electrosurgical validation study or a multicenter device trial.

Volume: An average breast model is approximately 500 mL in volume.

Total hydrogel: 1,000 phantoms × 500 mL = 500 Liters.

Biomaterial required: Gellan gum phantoms typically require a concentration of ~2% w/v (20 grams per Liter) to match the mechanical stiffness of soft tissue.

Total scale: 500 L × 20 g/L = 10 kg of pure gellan gum (b) How long? I would envision the project in four stages:

1. Material formulation and screening: approximately 2–3 months.
2. Automated phantom fabrication: approximately 2–3 months.
3. Electrical, mechanical, and thermal characterization: approximately 2–3 months.
4. Electrosurgical validation and optimization: approximately 2–4 months. This would correspond to an initial prototype development period of approximately 6–12 months.

And after atomation help:

Biomanufacturing Time: Sphingomonas elodea yields approximately 15 g/L of gellan in industrial fermentation. To produce 10 kg, we need a bioreactor working volume of roughly 660 Liters (accommodated by a standard 1,000 L bioreactor). A typical aerobic fermentation batch takes about 48 to 72 hours. Downstream processing (precipitation and drying) adds another 48 hours.

Automated Fabrication Time: Using a bioprinting array with 5 parallel extrusion robots, assuming each phantom takes 1 hour to print, the throughput is 5 phantoms/hour. Printing 1,000 phantoms would take 200 hours (about 8.3 days of continuous operation).

Total Time: Fermentation (3 days) + Downstream Processing (2 days) + Automated Printing (8.3 days) = approximately 13.3 days to go from bacterial culture to 1,000 finished electrosurgical phantoms.

5. What are the 2–3 biggest unknowns or bottlenecks?

1. Matching multiple tissue properties simultaneously

The first major unknown is whether a biologically produced material can simultaneously reproduce the electrical, mechanical, and thermal properties that are relevant to electrosurgery.

Changing one component of the formulation may improve one property while negatively affecting another. Although previous studies demonstrate that gellan-gum-based phantoms can have tunable properties, achieving the specific combination required for a breast-tissue model and electrosurgical applications remains an engineering challenge [1,3].

2. Reproducing the electrosurgical response of real tissue

An important question is whether the phantom can reproduce relevant phenomena such as electrical current distribution, heating, coagulation, cutting behavior, and thermal effects.

Previous work has demonstrated the feasibility of conductive tissue-mimicking phantoms for electrosurgical applications, but further validation would be required for a breast-tissue-specific model [2].

3. Reproducibility and durability

The final bottleneck is whether the phantom can be manufactured consistently and remain stable during storage and repeated testing.

If two phantoms produced from the same formulation behave differently, they would not provide a reliable platform for comparing electrosurgical devices or control strategies.

Therefore, batch-to-batch reproducibility, material stability, mechanical degradation, and changes in electrical properties during storage and repeated testing would need to be evaluated.

References

1. Chen, R. K., & Shih, A. J. (2013). Multi-modality gellan gum-based tissue-mimicking phantom with targeted mechanical, electrical, and thermal properties. Physics in Medicine & Biology, 58(16), 5511–5525. https://doi.org/10.1088/0031-9155/58/16/5511

2. Migliorini, F., et al. (2024). Conductive Gel Phantoms for Training in Electrosurgery. Advanced Materials Interfaces. https://doi.org/10.1002/admi.202400246

3. Brzozowski, P., Penev, K. I., & Mequanint, K. (2021). Gellan gum gel tissue phantoms and gel dosimeters with tunable electrical, mechanical and dosimetric properties. International Journal of Biological Macromolecules, 180, 332–338. https://doi.org/10.1016/j.ijbiomac.2021.03.047

Week 2 HW: DNA Design

DNA Design

Part 0: Set up in Kernel

The project workspace is established in the Kernel HTBAA 2026 workspace under the repository “HW 2 Malinche Mejia Gellan Tissue Phantom” and folder “Mejia-GellanPhantom”.

In Kernel, a Part is an individual DNA element used for genetic design, such as a promoter, ribosome binding site (RBS), coding sequence (CDS), or terminator. A Construct is a complete DNA design assembled from multiple Parts in a specific order.

For my project, the gelK coding sequence would be one Part. When it is combined with the appropriate regulatory elements, such as a promoter, RBS, and terminator, the resulting sequence represents the Construct.

Codon Optimization: Used to adapt a coding sequence to a specific host organism to improve compatibility with its expression system.

Biosecurity Sequence Scanner: Used to screen DNA sequences for potential biosecurity concerns or matches to sequences of concern.

Sequence Alignment: Used to compare two or more DNA or protein sequences and identify similarities and differences.

Part 1: Define your expression target

1. What molecule are you expressing?

I am expressing β-1,4-glucuronosyltransferase (GelK), a key glycosyltransferase enzyme in the gellan gum biosynthetic gene cluster.Because my project from Homework #1 produces a biomaterial (gellan gum exopolysaccharide) to build multilayer tissue-mimicking phantoms for electrosurgical testing, the material itself is synthesized by a multi-enzyme pathway rather than encoded directly by a single gene. The GelK enzyme catalyzes the critical step of transferring glucuronic acid onto the growing tetrasaccharide repeat unit ($[\rightarrow 4)\text{-}\beta\text{-D-Glcp}\text{-(1}\rightarrow 4)\text{-}\beta\text{-D-GlcA}\text{-(1}\rightarrow 4)\text{-}\beta\text{-D-Glcp}\text{-(1}\rightarrow 3)\text{-}\alpha\text{-L-Rhabp}\text{-(1}\rightarrow]$) that forms the structural backbone of gellan gum. Expressing and engineering this rate-limiting glycosyltransferase allows for optimized polysaccharide yield and downstream hydrogel property tuning.

2. What is the sequence?

Protein / Gene Target: Glucuronosyltransferase (gelK)

Source Organism: Sphingomonas elodea (formerly Sphingomonas paucimobilis ATCC 31461)

Database Accession: UniProtKB / Swiss-Prot Accession: Q93DT4 (NCBI Reference Sequence: YP_003022145.1)

Citation:

UniProt Consortium. (2023). Glucuronosyltransferase GelK - Sphingomonas paucimobilis. UniProtKB Entry Q93DT4. Harding, N. E., et al. (2004). Organization of genes required for gellan polysaccharide biosynthesis in Sphingomonas paucimobilis ATCC 31461. Journal of Industrial Microbiology and Biotechnology, 31(2), 70-82.

3. What host or chassis?

Host Chassis: Escherichia coli BL21(DE3)

Justification: While the native producer from HW1 is Sphingomonas elodea, E. coli BL21(DE3) is selected as the expression chassis for recombinant protein production and functional characterization. It provides robust growth, well-established T7 RNA polymerase-driven high-yield protein expression, and seamless compatibility with standard genetic circuit tools in Kernel.

4. Where does the product need to go?

Subcellular Destination: CytoplasmTargeting Sequence Requirement: No secretion signal peptide (such as PelB, OmpA, or PhoA) is attached.

Justification: GelK is an intracellular enzyme that functions at the inner membrane boundary on the cytoplasmic face, utilizing cytoplasmic activated sugar-nucleotide precursors (UDP-glucose and UDP-glucuronic acid) to assemble the repeat unit before membrane translocation. Therefore, the expressed recombinant protein must remain in the cytoplasm for activity assays and IMAC purification via the N-terminal $6\times\text{His-tag}$.

Part 2: Design your genetic parts

Part 3: Assemble your construct

The expression cassette for gelK (glucuronosyltransferase, UniProt: Q7X2M3) was designed for heterologous protein expression in Escherichia coli BL21(DE3) using the pET-28a(+) backbone architecture.The genetic architecture consists of five functional parts arranged in $5’\rightarrow 3’$ orientation:

1. pT7_Promoter: High-level, IPTG-inducible transcription driven by T7 RNA polymerase.

2. rbs_ShineDalgarno: Efficient ribosome binding site for translation initiation.

3. 6xHis_Tag (CDS +start -stop): Provides the initial start codon (ATG) and an N-terminal polyhistidine tag for downstream affinity purification via IMAC.

4. gelK_CDS (CDS -start +stop): The coding sequence for the glucuronosyltransferase enzyme, configured without an internal start codon to maintain the in-frame N-terminal fusion, ending with a standard stop codon (TGA).

5. T7_Terminator: Ensures transcription termination and mRNA stability.

Part 4: Optimize and quality-check

Codon Optimization

The gelK_CDS sequence was codon-optimized for expression in Escherichia coli BL21(DE3) using Kernel’s codon optimization tool, specifying constraints to avoid internal restriction sites (NdeI, XhoI, EcoRI, BamHI) and maintain an optimal GC content around ~52%. Organisms exhibit species-specific codon usage bias based on host tRNA abundances; replacing rare native codons from Sphingomonas elodea with preferred E. coli codons prevents ribosome stalling and improves translation efficiency. While the translated amino acid sequence remained 100% identical, silent nucleotide substitutions significantly improved codon adaptation for host expression while eliminating unwanted restriction cuts.

Sequence Search (Part Match / BLAST)

Running Kernel’s sequence search on gelK_CDS confirmed that this codon-optimized sequence represents a novel genetic part within our local workspace repository. To verify target identity against global biological databases, the translated primary sequence was cross-referenced with UniProt KB (Accession: Q7X2M3 / Q93DT4), confirming 100% amino acid identity to the glucuronosyltransferase gelK from Sphingomonas elodea. This ensures that the engineered CDS accurately encodes the target enzyme for gellan synthesis without unintended structural mutations.

Biosecurity & Safety Screening

Kernel’s automated safety screen and expression verification were executed on the complete pET28a_gelK_Expression_Cassette construct to screen for biosecurity hazards and expression compatibility in E. coli. The screen passed without flagging any biosecurity risks, confirming that the construct contains only benign laboratory control elements (T7 expression machinery, polyhistidine tag) and a metabolic enzyme from a non-pathogenic bacterium (Sphingomonas elodea). The construct fully complies with Biosafety Level 1 (BSL-1) research guidelines.

Part 5: From design to build

1. Assembly Strategy & Justification

For prototyping our pET28a_gelK_Expression_Cassette (~1.4 kb total length, comprising promoter, RBS, His-tag, gelK CDS, and terminator), we would order the insert as a single synthetic gBlock / double-stranded DNA fragment (dsDNA) and clone it into a linearized pET-28a(+) vector backbone using Gibson Assembly (Gibson et al., 2009).

• Length & Complexity Reasoning: At ~1,400 bp, the insert exceeds the standard length for direct single-oligo chemical synthesis, but falls well within the sweet spot for commercial gene synthesis providers (e.g., Twist Bioscience, IDT).
• Assembly Method: Rather than relying on traditional restriction-digestion and ligation—which introduces unwanted scar sequences and relies on specific cleavage sites—Gibson Assembly uses an exonuclease, polymerase, and ligase cocktail to seamlessly join overlapping dsDNA fragments (Gibson et al., 2009). Overlapping terminal sequences (20–40 bp) matching the pET-28a(+) insertion site ensure scarless, high-efficiency directional insertion.

2. Synthesis Constraints, Sequence Hazards, and Error Correction

Direct de novo chemical synthesis of oligonucleotides via phosphoramidite chemistry suffers from intrinsic coupling error rates (typically 1 error per 100–300 bp), primarily consisting of single-nucleotide deletions and substitutions (Carr et al., 2004).

• Sequence Hazards in Construct:
  • GC Content & Repetitions: The wild-type gelK gene from Sphingomonas elodea features a high GC content (>65%), which causes secondary structure formation (hairpins) during PCR amplification and synthesis. Our codon optimization step reduced extreme local GC peaks to ~52% and removed internal homopolymeric runs, significantly mitigating synthesis failure risks.
  • T7 Terminator: The strong stem-loop structure of the T7 terminator can cause DNA polymerase slip during synthesis or sequencing verification.
• Mitigation & Error Correction:
  • To obtain error-free construct clones from synthesized pools, enzymatic error correction protocols using mismatch-binding proteins (e.g., MutS) or mismatch-cleaving endonucleases (e.g., ErrASE / CelI) can be applied to digest heteroduplex errors prior to clonal amplification (Carr et al., 2004; Tian et al., 2004).
  • Clones are subsequently verified via Sanger or Next-Generation Sequencing (NGS) to guarantee an intact, frame-shift-free open reading frame (ORF).

3. Prototyping vs. Production at Scale

• Prototyping Phase (Single/Few Constructs):

  • Approach: Low-throughput, high-purity commercial dsDNA synthesis (gBlocks) coupled with benchtop Gibson Assembly or Golden Gate cloning into pET-28a(+).
  • Goal: Rapid turn-around time, individual Sanger sequence verification, and functional validation of protein expression and gellan precursor yield in E. coli BL21(DE3) at the bench scale.

• Production Phase at Scale (Combinatorial Libraries / High-Throughput Screening):

  • Approach: Multiplexed microchip-based oligo pool synthesis (e.g., high-density DNA microarrays or microfluidic chips) combined with selective PCR amplification and automated multiplex assembly (Tian et al., 2004; Kong et al., 2007; Kosuri et al., 2010).
  • Scaling Advantage: Microchip synthesis drastically lowers cost per base by miniaturizing reaction volumes into picoliter regimes (Kosuri et al., 2010). If we needed to screen hundreds of gelK codon variants, promoter strength combinations, or mutant libraries, microchip pool synthesis followed by enzymatic error correction (Tian et al., 2004) and high-throughput transformation would lower per-construct costs from hundreds of dollars to cents per gene.

Part 6: What don’t you know yet?

1. Inclusion Body Formation and Protein Folding Dynamics

• The Risk: High-level expression driven by the strong, IPTG-inducible pT7 promoter in Escherichia coli BL21(DE3) often leads to metabolic burden and rapid protein accumulation.
• The Unknown: As a membrane-associated glucuronosyltransferase originating from Sphingomonas elodea, recombinant GelK expressed at extreme levels may fail to fold correctly, resulting in insoluble inclusion bodies or misfolded, inactive protein.
• Empirical Validation Needed: We need to test whether lowering the growth temperature post-induction (e.g., $16^{\circ}\text{C}$ to $20^{\circ}\text{C}$) or reducing IPTG concentration (0.1 mM vs. 1.0 mM) is necessary to yield soluble, active enzyme.

2. Metabolic Load and Precursor Availability

• The Risk: Overexpressing an active glucuronosyltransferase consumes intracellular UDP-glucose and UDP-glucuronic acid pools within E. coli.
• The Unknown: Without co-optimizing the upstream nucleotide-sugar biosynthetic pathway (e.g., ugd or galU expression), gelK expression alone may become rate-limited by substrate availability, or severely disrupt host cell homeostasis and growth rate.
• Empirical Validation Needed: Metabolomic profiling and functional enzymatic assays are required to determine whether native precursor levels in E. coli are sufficient to achieve measurable gellan backbone synthesis.

3. Impact of the N-terminal 6xHis Tag on Enzymatic Activity

• The Risk: The addition of an N-terminal polyhistidine tag (6xHis_Tag) facilitates downstream IMAC purification and Western blot verification.
• The Unknown: It is unknown whether the spatial position of the 6xHis tag will sterically interfere with the active site or membrane-anchoring domain of GelK.
• Empirical Validation Needed: Comparative activity assays between the tagged variant and a native (untagged) control—or incorporating a cleavage site (e.g., Thrombin or TEV protease)—will be necessary if catalytic efficiency is compromised.

References

• Carr, P. A., Park, J. S., Lee, Y. J., Yu, T. J., Zhang, S., & Jacobson, J. M. (2004). Protein-mediated error correction for de novo DNA synthesis. Nucleic Acids Research, 32(20), e162. https://doi.org/10.1093/nar/gnh160
• Elowitz, M. B., & Leibler, S. (2000). A synthetic oscillatory network of transcriptional regulators. Nature, 403(6767), 335–338. https://doi.org/10.1038/35002125
• Gibson, D. G., Young, L., Chuang, R. Y., Venter, J. C., Hutchison, C. A., & Smith, H. O. (2009). Enzymatic assembly of DNA molecules up to several hundred kilobases. Nature Methods, 6(5), 343–345. https://doi.org/10.1038/nmeth.1318
• Kong, D. S., Carr, P. A., Chen, L., Zhang, S., & Jacobson, J. M. (2007). Parallel gene synthesis in a microfluidic device. Nucleic Acids Research, 35(8), e61. https://doi.org/10.1093/nar/gkm121
• Kosuri, S., Eroshenko, N., LeProust, E. M., Superbi, M., Lansdorp, J., & Church, G. M. (2010). Scalable gene synthesis by selective amplification of DNA pools from high-fidelity microchips. Nature Biotechnology, 28(12), 1295–1299. https://doi.org/10.1038/nbt.1716
• Stanton, B. C., Nielsen, A. A., Tamsir, A., Muchode, K. M., Voigt, C. A., & Brophy, J. A. (2014). Genomic mining of prokaryotic repressors for orthogonal logic gates. Nature Chemical Biology, 10(2), 99–105. https://doi.org/10.1038/nchembio.1411
• Tian, J., Gong, H., Sheng, N., Zhou, X., Gulari, E., Gao, X., & Zhou, X. (2004). Accurate multiplex gene synthesis from programmable DNA microchips. Nature, 432(7020), 1050–1054. https://doi.org/10.1038/nature03151

Week 3 HW: Cell-Free Protocols

Section 3: Cell-Free Reaction Design Assignment

A. Compare Cell-Free Reaction Compositions

1. Potassium Glutamate vs. Magnesium Glutamate

• Potassium Glutamate (K-Glu):

  • Role: Supplies K⁺ (monovalent cation) and glutamate⁻ (organic anion). K⁺ is required for ribosome stability, codon-anticodon decoding, and peptide bond formation. Glutamate mimics the natural intracellular anion environment of E. coli, minimizing enzyme denaturation compared to chloride (Cl⁻).
  • Concentration Range: Typically 60–180 mM (optimal around 100–130 mM).
  • Sensitivity: Possesses a broad bell-shaped curve. Moderate variations (±10–20 mM) lead to minor, gradual changes in translation yield.

• Magnesium Glutamate (Mg-Glu):

  • Role: Supplies Mg²⁺ (divalent cation), which stabilizes the 70S ribosome assembly, facilitates tRNA folding, catalyzes T7 RNA polymerase activity, and chelates NTPs to form active Mg-NTP complexes.
  • Concentration Range: Typically 4–20 mM (optimal around 8–12 mM).
  • Sensitivity: Extremely sensitive with a very narrow optimal window (±1–2 mM).

Why Magnesium is More Sensitive

1. Stoichiometric Chelating Competition: Free Mg²⁺ ions are tightly chelated by NTPs, NMPs, and inorganic phosphate (Pᵢ). Any alteration in NTP or energy system concentrations alters free Mg²⁺ availability.
2. Ribosomal Integrity vs. RNA Misfolding: At low free Mg²⁺ (<4 mM), 70S ribosomes dissociate into 30S and 50S subunits, stopping translation. At high free Mg²⁺ (>15–20 mM), translation misreading increases, non-specific RNA secondary structures are stabilized, and Mg₃(PO₄)₂ precipitates out of solution.

2. PEP + NTP vs. Ribose + NMP Reaction Formulations

• PEP + NTP System: Relies on phosphoenolpyruvate to rapidly phosphorylate ADP to ATP via pyruvate kinase. However, PEP and triphosphates (CTP/GTP/UTP) are very expensive. PEP breakdown rapidly releases inorganic phosphate (Pᵢ), which depletes free Mg²⁺ and lowers pH.
• Ribose + NMP System: Utilizes endogenous central metabolic pathways in crude lysate to convert cheap carbohydrate substrates and NMPs into active NTPs. This steady energy recycling prevents sudden phosphate accumulation spikes, lowers reaction cost, and extends reaction lifetime.

3. Literature Comparison: PANOx-SP vs. Modern Low-Cost CFPS

B. Design One Reaction Composition for Each Protein

Total Reaction Volume: 20,000 nL Fixed Components (9,000 nL): 5,000 nL Lysate (25%), 2,000 nL DNA, 2,000 nL Base Buffer. Variable Volume Budget: 11,000 nL (Reagents + Nuclease-Free Water).

1. sfGFP Composition (Fluorescent Reporter Baseline)

• Protein Attributes: 27 kDa, fast-folding β-barrel, 0 disulfide bonds.
• Redox Status: Standard reducing lysate (contains DTT).

2. PETase (TfCut2) Composition (1 Disulfide Bond)

• Protein Attributes: 28 kDa, serine hydrolase, 1 required disulfide bond (Cys241–Cys299).
• Redox Status: Mildly oxidizing / permissive redox environment.

3. Reteplase Composition (9 Disulfide Bonds)

• Protein Attributes: 39 kDa, truncated tPA (Kringle-2 + Protease domains), 9 required disulfide bonds.
• Redox Status: Strictly oxidizing DSB formulation (IAM-treated Δgor lysate, zero DTT, GSSG/GSH redox buffer, DsbC chaperone).

Community Reagent Contribution & Rationale

• Proposed Reagent: Trehalose (0.10–0.20 M final concentration)
• Category: Osmoprotectant / Chemical Chaperone
• Rationale: Trehalose is a non-reducing disaccharide that acts as a potent osmoprotectant and macromolecular crowding chaperone. In cell-free reactions, trehalose suppresses non-specific hydrophobic aggregation during co-translational folding without inhibiting ribosomal translation. It is particularly effective for complex target proteins with multiple disulfide bonds (such as Reteplase), increasing the yield of soluble, natively folded protein.
• Reference Link: Seki et al., Biotechnology and Bioengineering

Forum Post Summary

Proposal: High NTP costs and inorganic phosphate (Pᵢ) accumulation are major bottlenecks in CFPS. Pᵢ chelates free Mg²⁺, leading to premature termination of translation.

I propose evaluating a Polyphosphate Kinase (PPK) + Polyphosphate (PolyPₙ) energy regeneration system. PPK uses inexpensive polyphosphate to re-phosphorylate NMPs/NDPs into NTPs in situ.

Key Advantages:

1. Cost Reduction: Replaces expensive NTP/PEP stocks with low-cost polyphosphate (~$0.01/mL).
2. Physicochemical Stability: Maintains low steady-state concentrations of Pᵢ, preventing Mg²⁺ precipitation and extending reaction lifetime beyond 8 hours.

Helper Plasmid Design for Reteplase Folding

To maximize yields of functional 9-disulfide Reteplase, a T7 Polycistronic Helper Plasmid (pHELPER-DSB) can be added to the CFPS reaction:

1. DsbC: Disulfide bond isomerase that reshuffles incorrectly formed S-S bonds.
2. DsbA: Periplasmic thiol oxidase that rapidly introduces initial disulfide bonds.
3. Human PDI: Eukaryotic protein disulfide isomerase optimized for tissue plasminogen activator derivatives.
4. GroEL/GroES & DnaK/DnaJ: ATP-dependent chaperones that prevent aggregation during co-translational folding.

Final Project