Engineering

Build: The first iteration of the BLACKBIRD program set the groundwork for the rest of the iterations. To begin creating an automated pipeline, a FASTA file reader was implemented to read gene inserts provided to BLACKBIRD, as well as a program to read the Stealth results. Following these being parsed, every match from the Stealth file found in the gene insert recorded its location and the sequence in question. At the final stage of editing the gene insert, we elected to generate a sequence with a random alternate codon based on the desired amino acid. As multiple sequences would be generated from this approach, we would decide on a sequence to return based on ranking the generated sequences by least number of Stealth hits and percent identity.

Test:The first run of testing utilized the EGFP protein’s gene sequence for BLACKBIRD, seeing as it would be used as a screening marker in our wet lab experiments. The tests indicated various shortcomings of our initial prototype that helped us refine our goals in future cycles. Our implementation of the RMS site removal operated on generating alternate sequences for each Stealth site, saving them to be ranked later. We realized this would require us to create a program to identify the relevant codon to be altered, which we implemented by finding the codon which contained the first nucleotide of the cut site. After an alternate sequence was generated with random codon matching, the sequence was searched for the number of remaining sites, dubbed ‘STEALTH scores’, and sorted based on which sequence had the lowest. We additionally calculated the percent identity compared to the original gene insert sequence, to be utilized down the pipeline as another sorting method.

The output of BLACKBIRD containing a list of ranked sequences based on the number of RMS sites found within it, indicated at the end of each sequence with a negative number.

Learn:We recognized quickly that using random alternative codons would not be a viable solution in optimizing inserts to be more efficient, as too many Stealth sites remained in the sequence. Additional issues were discovered as the code outputted sequences of varying lengths, chalked up to errors from the random generation. We could not find a worthwhile way to integrate the percent identity to the original sequence as a ranking method, so we decided to discard this. The software development process required us to create new functionality we didn’t foresee in the Design period, such as the STEALTHParser module to convert Stealth outputs from IUPAC and the codon finder for edits. We attempted to delve more into the design process to anticipate developments like this going forward.

Design:The second cycle added a second member to the dry lab team and gave more time to design the process. We included and decided on new modules to be part of the pipeline, which were updated to include the parsing module. The newest iteration introduced a codon bias calculator for the target genome to optimize the gene insert for our target organism. Below is the updated pipeline:

Build:Various new Python modules and updated names were added to the BLACKBIRD build. The FASTA parser was named FASTAReader, and a STEALTHParser module was introduced that took the input of Stealth sites and converted the nomenclature from IUPAC standards. The codonUsage module derived a codon usage table from the whole genome file the program parsed. A StealthHits function was coded that searched through and found every instance of a Stealth hit in the gene insert, and finally, the codonChoice replaced the codons of the Stealth hit with the most biased codon from the codon usage table. This version did not require multiple sequences to be stored and ranked. Concerns were raised about maintaining variation in future versions of the code, as it would stochastically improve the transformation efficiency of the sequence. We additionally began working on a github repository for the drylab team to use throughout. This was separate from the 2024 iGEM gitlab, which was activated later in the process.

Test:Testing the the new version of BLACKBIRD found improvements that stuck into later versions and were built on in following cycles. The respective module successfully generated the codon usage tables; however, we faced hurdles when referencing it for changes. The codonChoice module occasionally struggled to generate alternate sequences, specifically for Alanine, which it often substituted instead of the correct amino acid. The output returned only a single sequence, but it still contained a Stealth score that was too high.

The output of codonUsage, that shows the codon use and bias of the genome given to BLACKBIRD.

Learn:We discovered in this iteration that we needed to create a new methodology for editing the gene insert. The errors caused by the codonChoice module were likely indicative of other issues with the gene insert modification process. We were posed with two new hurdles: the existence of rare codons and the mismatch between the host genome from which the gene insert was derived and the target genome, as well as the program possibly creating new RMS sites with every new addition. We began work on a solution to the codon bias problem as the issue of introducing Stealth sites was brainstormed.

Design:The third cycle of the BLACKBIRD design process was to rework the codon bias tables and the codon selection process, as well as added domestication protocols to fit the iGEM standard for parts. The pipeline was changed to run the codon bias calculations on both genomes, and the codonChoice module was altered to fit this. Below is the pipeline

Build:The main change added to this build of the program was the integration of the codon bias ranking system into the codonUsage module. The module would additionally create a ranking that matched the most used codons for an amino acid in the host genome to the same in the target genome. This would replicate the conditions of the host organism within the target organism, as the gene insert from the host would be adapted to the target’s codon biases, accounting for rare codons.
Additionally, we researched the best ways to adapt to iGEM part registry’s standards as well as for our Golden Gate experiments we would run. A simple solution for removing Type IIs enzyme sites as well as Golden Gate cut sites was to add them manually to our Stealth Output list; the sequences contained within the output list were to be removed, and utilizing this eased our workload.

Test: The alterations to the codonChoices module led to successful modifications to our gene insert of choice. We no longer had errors that led to mistranslating codons as we did before. However, our alternative sequences were still unfit for transformation with their high Stealth scores.

Learn: As the Stealth scores for our sequence remained high, we investigated further and determined that the main cause of these were Stealth sites generated at the sites of previous Stealth site alterations. As we developed the code from this DBTL cycle, we had been designing the mechanism for changing Stealth sites while preventing the generation of any new sequences. In addition to this we had considered our solution for domestication, and concluded that would require a more professional solution in time, as the current solution would not be functional in a package workflow.

Design:

Build:

Test:

Learn: