Week 2 HW: Genetic Design in Kernel

Genetic Construct Design

Name: Sergio Cuiza Repository: cuiza-mycelium-panels Folder: HTBAA_HW2_Sergio_Cuiza / Cuiza-mycelium-panels

Part 0: Setup

I created the repository cuiza-mycelium-panels and the folder HTBAA_HW2_Sergio_Cuiza / Cuiza-mycelium-panels in the Kernel HTBAA 2026 workspace. This organization keeps all parts, sequences, and constructs for my project in one centralized location, making it easier to track versions and share work with collaborators.

Part 1: Expression Target (Rationale)

For this design exercise, I chose to express Annexin II, a eukaryotic protein.

• Why this molecule? Annexin II is a calcium-dependent phospholipid-binding protein involved in membrane trafficking. While my original HW1 project focused on fungal mycelium panels, I used Annexin II as a placeholder CDS to practice the full genetic assembly workflow. It represents a typical eukaryotic gene of interest (GOI) that requires careful codon optimization and proper regulatory elements. Working with a eukaryotic protein also highlights the challenges of expressing non-native proteins in a prokaryotic host.
• Sequence Source: NCBI accession M60769.1 (Xenopus laevis Annexin II mRNA).
• Host/Chassis: E. coli was chosen because it is the most well-characterized chassis for biomanufacturing. It grows quickly (doubling time ~20 minutes), is cheap to culture, and has a vast library of available genetic parts.
• Localization: Cytoplasm. Since Annexin II functions intracellularly, no signal peptide was added to the design. However, I acknowledge that eukaryotic proteins often require post-translational modifications that E. coli cannot provide.

Part 2: Genetic Parts (Justification)

Every genetic part was chosen to ensure stable maintenance and efficient transcription in the host.

Assembly Logic: The parts were chosen to function together as a single expression cassette. The promoter drives transcription, the CDS is translated into protein, and the terminator ensures the RNA polymerase stops. The backbone allows the construct to replicate inside E. coli and provides a way to select only the bacteria that took up the plasmid.

Part 3: Assembled Construct

• Construct Name: Annexin_II_expression_cassette
• Order (5’ to 3’): gpdA_promoter → Annexin_II_CDS_optimized → T7_terminator → pUC19_backbone
• How it was assembled: In Kernel, I created individual parts and then combined them into a single construct using the “Part match” tool. The parts were arranged in the natural 5’ to 3’ order of transcription. The reading frame was checked in the Sequence Viewer to ensure no internal stop codons were introduced, which would truncate the protein.

Figure 1. Schematic map of the construct generated in Kernel. The sequential order of elements is shown: promoter, optimized CDS, terminator, and backbone.

Figure 2. Circular map of the construct, showing the layout of the complete plasmid. The circular view helps visualize how the elements are connected in the physical DNA molecule.

Figure 3. Linear map of the construct, displaying the sequential arrangement of genetic elements from 5' to 3' without the circular distortion. This view is useful for verifying the exact order of parts.

Figure 4. Sequence viewer of the construct, where the reading frame integrity was verified. This ensures the start codon is in frame with the stop codon.

Part 4: Optimize and Quality-Check

1. Codon Optimization: I ran Kernel’s codon optimization tool (Optimizer: SIMPLE_0_3) specifically for E. coli. Why? Different organisms have different abundances of tRNAs. The original Xenopus laevis sequence uses codons that are rare in E. coli, which would cause ribosome stalling and low protein yield. The optimizer changed the DNA sequence to match E. coli’s preferred codons. It also included stop codons (TGA, TAA) and avoided the internal restriction site AbaB8342IV to facilitate future cloning steps. Result: I saved the new sequence as Annexin_II_CDS_optimized and replaced the original CDS in the construct.

Figure 5. Codon optimization result. Kernel compared the initial sequence (Xenopus) with the optimized one (E. coli). Note the change in nucleotide sequence without altering the protein sequence.

2. Sequence Alignment (Quality Check): I ran Kernel’s Pairwise Alignment tool to compare my optimized CDS (897 bp) against the original mRNA (1205 bp). Result: The pairwise identity is 45% at the nucleotide level. This low percentage is expected and correct. Codon optimization intentionally changes the DNA letters (A, T, C, G) while keeping the amino acid sequence identical. The detected insertions/deletions correspond to the untranslated regions (UTRs) present in the original mRNA but absent in the synthetic CDS. The alignment confirms the integrity of the coding region. I have included three screenshots to show the full length of the alignment.

Figure 6. Pairwise alignment (Part 1). Initial view of the nucleotide sequence comparison.

Figure 7. Pairwise alignment (Part 2). Continuation of the alignment showing further nucleotide changes due to codon optimization.

Figure 8. Pairwise alignment (Part 3). Final section of the alignment. The translated protein remains identical to the original.

3. Safety Screen (Biosecurity): I ran the Biosecurity Sequence Scanner (SecureDNA) on the full assembled construct. Result: The scanner reported “No flagged sequences detected”. This confirms my construct does not contain any sequences associated with potential biosecurity threats. This is expected because the construct only contains standard regulatory elements (promoter, terminator, backbone) and a normal eukaryotic protein. The scanner checks against databases of regulated pathogens and toxins to ensure the sequence cannot be misused.

Figure 9. Biosecurity scan result (SecureDNA). No dangerous sequences were detected.

Part 5: From Design to Build

A sequence in software is not yet physical DNA. Here is a detailed breakdown of how I would build it, considering both prototyping and scale-up.

Prototype (One construct): For a single test construct, I would order it as synthetic DNA from a vendor like Twist Bioscience or IDT. At ~4.5 kb, it is well within the standard synthesis limit (usually up to 5-10 kb). This is the fastest and most reliable method because it eliminates the need for multiple cloning steps. Alternative: If I wanted to save money, I could use Gibson Assembly. This involves designing 20-40 bp overlapping sequences at the ends of the promoter, CDS, terminator, and backbone. The fragments are mixed with an exonuclease, polymerase, and ligase, which seamlessly join them. This is cost-effective for testing a single design and allows for easy swapping of parts.

Synthesis Constraints: Synthesis has error rates, typically 1 error per 1,000-3,000 bp. My construct contains no highly repetitive sequences, which are notoriously hard to synthesize. However, if I had repeats, I would need to split the synthesis into smaller fragments or use codon variants to break the repeats without changing the protein.

Scale-up (Industrial Production): If I needed to produce this construct at an industrial scale, I would use chip-based oligo synthesis (Tian et al., 2004). This technology synthesizes millions of oligonucleotides on a microchip in parallel. Error Correction: Because chip synthesis has high error rates, I would use protein-mediated error correction (Carr et al., 2004). This involves using mismatch-binding proteins (like MutS) to bind to and remove incorrectly synthesized DNA, leaving only the perfect sequences. This allows for the parallel synthesis of thousands of variants, which is useful if I wanted to optimize the promoter or RBS further.

Citations:

• Carr, P. A. et al. (2004). Protein-mediated error correction for de novo DNA synthesis. Nucleic Acids Research, 32, e162.
• Gibson, D. G. et al. (2009). Enzymatic assembly of DNA molecules up to several hundred kilobases. Nature Methods, 6, 343–345.

Part 6: What don’t I know yet?

Even though the design is complete, there are still major biological risks and unknowns that would need experimental testing.

1. Promoter Compatibility (Major Flaw):

   • The Risk: I used the gpdA promoter (fungal) in an E. coli host. E. coli RNA polymerase will not recognize this promoter. This means the gene will not be transcribed, and no protein will be produced.
   • How to test: I would clone the CDS downstream of a T7 promoter (for BL21(DE3) cells) or a lac promoter (for standard strains). I would measure protein yield using SDS-PAGE or a fluorescent reporter.

2. Protein Folding and Solubility:

   • The Risk: Annexin II is a eukaryotic protein. It may not fold correctly in E. coli without specific chaperones, potentially forming inactive inclusion bodies (misfolded protein aggregates).
   • How to test: I would lyse the cells and run SDS-PAGE to see if the protein is in the soluble fraction or the pellet. If it’s in the pellet, I would try lowering the induction temperature (e.g., 16°C) or co-expressing chaperones (GroEL/ES).

3. Codon Optimization Sufficiency:

   • The Risk: While codon optimization improves translation, it does not guarantee high yield. Other factors like mRNA secondary structure can hinder ribosome binding.
   • How to test: I would measure protein levels via Western blot. If low, I would try different 5’ UTR sequences or a stronger RBS to improve translation initiation.

4. Plasmid Stability and Toxicity:

   • The Risk: Leaky expression of a foreign protein can be toxic to E. coli, causing the cells to lose the plasmid or die before induction.
   • How to test: I would use a tightly controlled promoter (like pET vectors with the lacI repressor) or a low-copy backbone to minimize basal expression. I would also check plasmid retention by plating on ampicillin plates.

AI Citation

AI client: DeepSeek. Prompts used: Assisted with choosing genetic parts, formatting the Kernel workflow, centering images in Markdown, and drafting the write-up with biological justifications. All biological decisions and sequences were verified by me.