Part:BBa_K5102080

pRAM_syntheticUTR-ProgRAM-recording-tape3.0

The composite part pRAM_syntheticUTR-ProgRAM-recording-tape3.0 (Part:BBa_K5102080) is designed for the ProgRAM molecular recording system. The core of our system uses dCas13, a dead RNA-targeting dPspCas13b enzyme coupled with an ADAR deaminase domain, which has shown high specificity and minimal off-target effects in RNA-editing systems such as “REPAIR” (Cox et al., 2017).Upon sensing a cellular event, dCas13 guides ADAR to the target RNA, catalyzing the single base conversion of adenosine (A) to inosine (I), which is recognized as guanine (G) during translation (Rees & Liu, 2018; Booth et al., 2023). This process enables RNA to serve as a dynamic recording tape, where each modification represents a logged event.

Part Design

The composite part features a recording tape composed of a series of START codons in the context of Kozak consensus arranged in three forward open reading frames (ORFs). In the composite part, the tape is in state 0, indicating that no molecular events have been recorded, and all adenosines in the START codons remain unmodified by dPspCas13b-ADAR2DD fusion. Downstream of the tape, a sequence of fluorescent proteins enables visualization of the current recorded state. The design incorporates sequentially modifiable adenosine sites within the RNA tape, creating a dynamic “traffic light” system that allows for precise in vivo monitoring of recording events without disrupting cellular functions. Each adenosine corresponds to a START codon; upon deamination, this modification disrupts the codon, shifting the open reading frame by one base pair and triggering the expression of one of three distinct fluorescent proteins: miRFP670nano3, mScarlet3, or mTagBFP2.

The frameshift-driven translation of multiple fluorescent proteins (XFPs) from a single construct required codon optimization in all three forward open reading frames, eliminating premature STOP codons, non-RFC compliant restriction sites, splice sites, repeats, or other sequences that could negatively affect mRNA stability were present. To address the challenge of optimizing codons across multiple reading frames, we developed a custom evolutionary algorithm based on Multi-objective Optimization (MOO) using the NGSA-III approach, implemented with the DEAP Python framework. The algorithm simulates natural evolution, starting from generating 500 back-translated DNA sequences and applying variations through mutation and mating, exchanging parts through crossover. With expert guidance, we fine-tuned our codon optimization algorithm, applying selection pressure over 300 generations, using metrics like codon adaptation index, frequency of optimal triplets, and exclusion of stop codon. More details about the codon optimization tool can be found on: https://2024.igem.wiki/munich/model/.

For continuous and reliable translational-level control of total protein expression across our system, we incorporated eUnaG (Truong et al., 2024), a small green fluorescent protein codon-optimized for all three ORFs.To ensure proper protein folding, each fluorescent protein is encoded downstream of the RNA tape and preceded by a 2A peptide, which promotes ribosomal skipping during translation.

During recording tape design part design careful considerations have been taken to ensure high editing rates, as well as specificity of gRNA binding. The molecular recording utilized in the project is based on the REPAIR v2 system developed by Cox et al., 2017. It has previously been shown that both the nucleotides surrounding the modified adenosine, called central base triplet (CBT), as well as location of the modified A within the sequence play an important role in the editing efficiency. For 30 nucleotide gRNA spacer design, 22nd and 28th positions have been shown to be the most efficient. Introduction of a A12G mismatch showed increased binding disruption of gRNA, ensuring highly selectable editing. While several Cas enzymes require a highly conserved Protospacer adjacent motif (PAM) sequence, PspCas13b does not require it for its activity. However, it still shows a preference for targets with protospacer flanking sites (PFSs).

After applying all of these considerations, we were left with the following minimal tapes design:

The missing nucleotides were designed in a way that: (i) N could be replaced by any nucleotide but adenosine, (ii) the gRNA complementary to the binding tape must have formed a conserved stem loop structure required by PspCas13b, which binds the ADAR2DD to the target deamination site. To ensure best design, to this end we have employed computational methods.

To enhance mRNA translation, we included a 5' synthetic untranslated region (UTR) - BBa_K5102065. We designed the UTR by utilizing a deep learning model developed by (Castillo-Hair et al., 2024), which is designed to optimize 5’UTRs for efficient mRNA translation. This model employs generative neural networks combined with gradient descent and is trained on polysome profiling data from randomized 5’UTR libraries across multiple cell types. This training allows the model to identify sequence features that significantly enhance translation efficiency.We input our target coding sequence into the model, which then generated optimized 5’UTR sequences that aimed to maximize translation efficiency. The optimization process accounted for various factors affecting translation initiation, including Kozak consensus sequences, upstream open reading frames (uORFs), and RNA secondary structures. By applying gradient descent, the model iteratively adjusted the UTR sequences to reduce potential impediments to ribosome binding and scanning, such as stable secondary structures. To validate the model’s predictions, we calculated the mean ribosome load (MRL) for each designed UTR, serving as an indicator of translation efficiency. We also assessed the minimum free energy (MFE) of these sequences using RNA folding prediction tools like RNAfold (Lorenz et al., 2011), where a lower MFE suggests fewer stable secondary structures that might negatively impact translation. Our analyses confirmed that the optimized UTRs had minimal secondary structures that could hinder translation initiation.

Additionally the composite part contains several elements ensuring high expression and stability of transcript:

• 3'UTR (4241–4284): 3' untranslated region. In the current instance, human beta-globin 3'UTR BBa_K5102053.
• WPRE (4382–4979): woodchuck hepatitis virus posttranscriptional regulatory element BBa_K5102069 for increasing transgene expression by minimizing readthrough transcription and improving termination.

The rest of the features of the composite part include:

• Mammalian promoter: promoter for plasmid expression of ProgRAM in a mammalian cell line. In the current instance, a CMV enhancer BBa_K5102067 and a CMV promotor BBa_K2217006. Alternative mammalian promoters driving the expression are possible, for instance, BBa_J433025, BBa_J433001, or BBa_J433002.
• T7 promoter (681–698): phage T7 promoter BBa_K3633015 for in vitro expression of the ProgRAM.
• Aptamer (4295-4317): an RNA aptamer acting as an anchor, enabling tape pulldown or localization. In the current instance, PP7 phage aptamer BBa_K5102054.
• T7 terminator (4334–4381): phage T7 transcriptional terminator BBa_K731721 for in vitro expression of the ProgRAM composite part.
• polyA (4984–5105): a polyadenylation site for mammalian transcription termination and RNA polyadenylation. In the current instance, SV40 polyA BBa_K2217005. Alternative polyA sites could be used.

Structural modelling of tape’s 3’UTR

ProgRAM is a complex system, yet it offers significant modularity and supports the incorporation of various add-ons, as outlined on the Design page. This flexibility is particularly evident in the 3’ UTR, which can accommodate additional elements with minimal impact on deamination or reporter expression. This region of the RNA is highly suitable for introducing RNA elements such as barcodes, ribozymes, or RNA aptamers. To ensure that the ProgRAM design can integrate elements into its 3’UTR while preserving their structural integrity, we conducted an in silico study. In this analysis, we structurally modeled our tape with the addition of the Okra aptamer system (BBa_K5102070), which we obtained as a tool for RNA visualization and quantification. The Okra aptamer is a bright and stable aptamer designed for mRNA imaging. Our structural modeling (as demonstrated with Okra-appended BBa_K5102073) confirmed that all eight fluorophore-binding sites in the 796 bp 4xdOkra region were preserved in their original conformation, with minimal structural abberations.

Visualisation of ViennaRNA folding results (Lorenz et al., 2011) of BBa_K5102080 using VARNA (Darty et al., 2009). RNA tape regions are highlighted as follows (from 5’ to 3’): 5’UTR in gray, recording tape 2.0 in gold, miRFP670nano3 in dark red, mScarlet3 in bright red, mTagBFP2 in light blue, eUnaG in pale green, WPRE in magenta, 4xdOkra in purple.

Cloning

The composite was assembled from gblocks and oligos provided by a DNA synthesis provider.

Cloning of our final constructs, pRAM_ProgRAM-recording-tapes were proceeded by many cloning steps. First, it included the creation of a minimal vector for SynBio applications, pRAM (BBa_K5102000). The construction of pRAM began with a pcDNA3.4-TOPO vector available to us in the lab and the vector was obtained by several Gibson assembly and KLD reactions. In the end, the backbone includes a CMV enhancer, promoter and 5'UTR, T7 promoter and terminator, Woodchuck posttranscriptional regulatory element (WPRE), SV40 polyA element, plasmid ori, as well as AmpR promoter and CDS for selection. In the end, two BsmBI-v2 recognition sites have been introduced to allow for Golden Gate assembly. Due to the high sequence similarity of the P2A-eUnaG-T2A sequences, cloning of the pRAM_ProgRAM-recording-tapes vectors were assembled together in a single tube Golden-Gate reaction using BsmBI-v2 enzyme. The reaction included pRAM backbone, ProgRAM recording tape, and three gBlocks: T2A-miRFP670nano3-P2A-eUnaG, T2A-mScarlet3-P2A-eUnaG, T2A-mTagBFP2-P2A-eUnaG.

Following E. coli transformation, eight colonies per construct were picked and screened by colony PCR, using a forward primer binding to the plasmid backbone and a reverse primer complementary to the insert. The results were verified via DNA electrophoresis, and colonies with the expected band size were used to inoculate overnight E. coli cultures. The next day, plasmid DNA was miniprepped and verified by Whole Plasmid Sequencing.

References

Booth, B. J., Nourreddine, S., Katrekar, D., Savva, Y., Bose, D., Long, T. J., Huss, D. J., & Mali, P. (2023). RNA editing: Expanding the potential of RNA therapeutics. Molecular Therapy, 31(6), 1533–1549. https://doi.org/10.1016/j.ymthe.2023.01.005

Cox, D. B. T., Gootenberg, J. S., Abudayyeh, O. O., Franklin, B., Kellner, M. J., Joung, J., & Zhang, F. (2017). RNA editing with CRISPR-Cas13. Science, 358(6366), 1019–1027. https://doi.org/10.1126/science.aaq0180

Truong, D.-J. J. et al. Exonuclease-enhanced prime editors. Nat Methods 21, 455–464 (2024).

Darty, K., Denise, A., & Ponty, Y. (2009). VARNA: Interactive drawing and editing of the RNA secondary structure. Bioinformatics, 25(15), 1974–1975. https://doi.org/10.1093/bioinformatics/btp250

Lorenz, R., Bernhart, S. H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2011). ViennaRNA Package 2.0. Algorithms for Molecular Biology, 6(1), 26. https://doi.org/10.1186/1748-7188-6-26

Rees, H. A., & Liu, D. R. (2018). Base editing: Precision chemistry on the genome and transcriptome of living cells. Nature Reviews. Genetics, 19(12), 770–788. https://doi.org/10.1038/s41576-018-0059-1

Sequence and Features

Assembly Compatibility:

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• 1000
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