iGEM 2024 — Team ucsc

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## Notebook Overviews

![](https://static.igem.wiki/teams/5149/notebook-imgs/notebook-july.png)

#### July Notes

##### Week of 07/01/2024

* Wet Lab:
  + Made all of the ingredients for BG-11 media
* Dry Lab:
  + Started research into transformation of L. Fusiformis
  + Discovered transformation protocol from Lumen Biosciences
* Human Practice:
  + Reached out and interviewed Olivia Mayer and Lydia Mapstone for community outreach
  + Reached out and interviewed Mckenna Hink for advice for working with Limnospira fusiformis
  + Reached out and interviewed Naama Aviram to further understand Cas10 mechanisms

##### Week of 07/08/2024

* Dry Lab:
  + Researched native promoters and terminators to be used in L. Fusiformis
  + Started development of BLACKBIRD to create optimized inserts
  + Discovered the Cyanogate system, which contained a variety of parts for use in cyanobacteria
* Wet Lab:
  + Made BG-11 media
  + Made LB+Amp media/plates for plasmids (pDV002, pDV044, pDV052)
  + Streaked plasmids on plates to grow up in incubator
  + Picked colonies to grow up in liquid culture, checked densities

##### Week of 07/15/2024

* Dry Lab:
  + Discovered the MoClo toolkit through our research of cyanogate
  + Learned how the Type IIS restrictions enzymes are used in Golden Gate reactions, which sets the foundation for learning how the MoClo toolkit works
  + Split our workload between the Lumen Biosciences protocol and the MoClo toolkit
  + Decided to work in parallel with other cyanobacteria with shorter doubling times, notably UTEX 2973
* Wet Lab:
  + Continued growing plasmids to check densities
  + Received A.platensis and figured out how to store
  + Started growing A.platensis in BG-11 in incubator
  + Made glycerol stocks and snap froze plasmids
  + Spun down/froze plasmids for miniprep

##### Week of 07/22/2024

* Dry Lab:
  + Started in silico modeling of Cyanogate system
  + Created basic constructs, including level 0 parts with existing inserts and optimized inserts
* Wet Lab:
  + Miniprepped plasmids, got strange results
  + Made LB+Cam media
  + Inoculated old plasmids (pDV002, pDV044, pDV052) & new plasmids (pRK24, pRL528)
  + Checked densities of plasmids
  + Plasmids spun down into glycerol stocks, others spun down for minipreps
  + Miniprepped twice – still not getting good results
  + Streaked more plates for pRK24 & pRL528
  + Made more LB+Cam plates
  + Miniprepped pDV002, pDV044, pDV052, still bad results
  + Inoculated pRK24, pLR528, checked OD’s
  + Made three more plates of L. fusiformis
* Education:
  + Girls in Engineering Presentation
  + Interviewed Ravendran Vasudevan for MoClo related questions

##### Week of 07/29/2024

* Dry Lab:
  + Split into groups: Single Plasmid, Double Plasmid, Argonaute, Limnospira
* Wet Lab:
  + Made more LB+Amp plates/liquid media
  + Streaked pDV052, pDV044, pDV002 plates to grow overnight
  + Miniprepped pDV002, got first good results
  + Inoculated pDV052 & pDV044 overnight
  + Made new BG-11 media
  + Miniprepped pDV044 & pDV052, still bad results
  + Plated parts of MoClo kit
  + Made spectinomycin media for plates/liquid culture
  + Took single colonies from MoClo plates and inoculated them, passaged them at the end of the day
  + Increased A.platensis growing temperature from 23℃ to 37℃
  + Diluted primers & made working stocks of rest for wet lab
  + Plated multiple CyanoGate parts
  + Passaged MoClo parts at the end of the day for minipreps
* Human Practice:
  + Interviewed Shan Huang for Argonaute related questions
  + Interviewed Alistair McCormick for MoClo related questions

## July Overview

In July, our main focus has been on Reagent Prepping, Project Development, and Human Practice.

## August Overview

In August, our main focus has been on Protocol Troubleshooting, growing out MoClo parts, and more Human Practice Background Research

![](https://static.igem.wiki/teams/5149/notebook-imgs/notebook-aug.png)

#### August Notes

##### Week of 08/05/2024

* Wet Lab:
  + Made LB+Spec plates/media
  + Made SOC media
  + Made new Membrane Wash Buffer – improved miniprep results
  + Took inventory of MoClo and CyanoGate parts that are grown out
  + Reinoculated any leftover parts from both kits that had low concentrations from the mini-preps
  + Researched optimal culture conditions for L.fusiformis; grew under a new light
  + Created a timeline for the “Wet lab” portion of the thesis
* Human Practice:
  + Reached out and interviewed Susan Golden for BG-11 related questions

##### Week of 08/12/2024

* Wet Lab:
  + Miniprepped side by side with Brenda and Dr. B to diagnose repeated poor miniprep results
  + Learned that multiple miniprep buffers aren’t good, made note of the ones that are
  + Did a mass miniprep with new and improved protocol, started getting multiple good values/graphs for our parts
  + Made TBE and ran gel to make sure DNA we’re getting from minipreps is the right size
  + Ran the gel of all of the seven successful samples from yesterday → we have plasmids!!
  + Miniprepped two samples inoculated overnight
  + Inoculated all leftover CyanoGate parts (~10 parts) for more miniprep
  + Diluted primers
  + Miniprepped 12 samples that were inoculated the previous night, a few need salt clean up
  + Spun down samples for future minipreps & made glycerol stocks of successful minipreps
  + Made more LB+Amp & LB+Spec media
  + Nanodropped all of our minipreps to have a coherent list of values
  + Started on MagBead protocol, made new TE buffer
* Human Practice:
  + Did a zoom social with Georgia State & Southwest Jiaotong University iGEM team to share experiences

##### Week of 08/19/2024

* Wet Lab:
  + Performed PCR reactions on leftover parts in order to amplify
  + Performed our first Golden Gate assembly reaction with our unoptimized GFP and Cpf1 inserts
  + Learned about blue-white selection process needed to select for transformed colonies
  + Made plates containing LB media, X-Gal, IPTG, and Spectinomycin for blue-white colony selection
  + Completed write-ups for the wet-lab section of the thesis
  + Ran MagBead protocol
  + Ran 0.8% gel to test MagBead – got a smear (DNA fragments, bad results)
  + Started troubleshooting MagBead protocol, added DNA Shield
  + Continued getting poor MagBead results (peak at 240)

##### Week of 08/26/2024

* Wet Lab:
  + Continued bizarre MagBead results, tried ‘high salt protocol for genomic prep’ to get higher A260/A230 values
  + Also started running Quick DNA HMW MagBead protocol with and without lysis buffer, without lysis buffer = best result
  + Ran gel to verify Quick DNA HMW MagBead protocol – correct band length, big band at the top to confirm genomic DNA
  + After gel verification, ran ethanol precipitation to improve the A260/A230 value
  + Diluted new primers
  + Started electrocompetent protocol and electroporated E.coli cells with Golden Gate Assembly reactions
  + Ran Q5 PCR on cultures from electroporation
  + Switched over to chemi-competent transformation with Top10 E.coli cells after discovering that salt from enzymes made electroporation an infeasible option
* Education:
  + Soquel Biotech High School meetup

![](https://static.igem.wiki/teams/5149/notebook-imgs/notebook-sept.png)

#### September Notes

##### Week of 09/02/2024

* Wet Lab:
  + For single-plasmid team, troubleshooted the PCR protocol using OneTaq 2X Master Mix
  + Ran an experiment using un-optimized PET28 GFP plasmid using both flanking and GFP-specific primers, with a subsequent gel electrophoresis
  + Took an inventory of the mini-prepped parts that we had, and grew out any parts that we did not already have saved in its eluted form
  + Received and rehydrated our optimized inserts for both Argonaute and Single Plasmid team
  + Did a Golden Gate Assembly reaction using the optimized inserts
  + For Argonaute team, inoculated CbAgo, Down homology arm (HA), Chi site, and C products
  + Performed a chemi-competent transformation
  + Ran a touchdown colony PCR using OneTaq with optimized inserts, with a subsequent gel electrophoresis and PCR clean up (for Argonaute PCR products)
  + Discovered that the recipe for X-Gal plates was wrong, found another recipe that used a much higher concentration of X-Gal that would hopefully increase the efficiency of the blue-white screening process
* Other:
  + Promotional project video filmed and edited

##### Week of 09/09/2024

* Wet Lab:
  + Using the colony PCR results from last week, ran a gel electrophoresis
  + Was getting subpar results on our gels, with little to no visualization for the fragments
  + Troubleshooted our gel electrophoresis, ran many diagnostic gels testing the following variables:
    - Viability of the loading dyes
    - Amount of sample to be loaded
    - Type of MasterMix used (OneTaq or Q5)
  + Eventually was able to reach a point of visualization for our PCR products on our gels, but was unable to get the right sizes
  + Troubleshooted the rest of our pipeline: colony PCR and Golden Gate Assembly
  + For Golden Gate Assembly, tested if the molar ratio of the insert made a difference (tested 3:1 and 4:1 as opposed to 2:1 for the Argonaute inserts)
  + From doing a Golden Gate Assembly with the backbone and no insert, discovered that the backbone was religating back to itself and some of the colonies did not contain the LacZ fragment at all
  + Started running inverse PCR on double plasmid backbone pC1.509

##### Week of 09/16/2024

* Wet Lab:
  + Ran a Q5 PCR for a Cpf1 mini-prepped sample, then ran a gel
  + Came to the discovery that Top10 cells were resistant to the antibiotic Streptomycin, which was our antibiotic resistance marker
  + Started to use DH5-𝝰 cells instead for transformation
  + Ran a Q5 PCR on several samples of the pICH41308 backbone in order to see which samples contained the LacZ fragment
  + Plated double plasmid parts and checked that all the other ones were already mini prepped
  + Inoculated double plasmid parts to be miniprepped the next day
  + Miniprepped double plasmid parts – got good graphs & values
  + Reamplified PCR of inverse backbone pC1.509 for double plasmid system from last week
  + Planned to send out an order for sequencing to Berkeley of our Cpf1 and empty backbone PCR products
  + Started a Level 1 Golden Gate Assembly reaction for the antibiotic-resistance level 0 part

##### Week of 09/23/2024

* Wet Lab:
  + Ran THE experiment to test if our restriction cutter and ligase worked
  + Experiment was golden gating 3 separate samples:
    - 1 - T4 buffer, backbone, insert, water
    - 2 - T4 buffer, backbone, insert, restriction cutter, water
    - 3 - T4 buffer, backbone, insert, restriction cutter, ligase, water
  + Saw growth on 2 which was not expected, so we decided that our cells could be bad, the plates could be bad, our restriction enzyme could be bad (prob not tho cus it’s new), or our strep could be bad
  + Found another box of DH5a comp cells
  + Plated DH5a comp. Cells onto LB + x-gal + spec, both boxes to test if either of them grew on spec
  + Both ended up growing, so we determined our plates were prob bad
  + Need to make new plates and ordered new spec as well
  + Came in too late for chemicomp protocol, OD600 needed to be around 0.55 and was 1.0 when checked
  + Optimal growth period is 8 hrs in starting culture, then 10 hrs for inoculations
  + Started to grow up DH5a cells from original bottle to make our own comp cells
  + For double plasmid team, ran our first golden gate reaction
  + Prepared for transformation after golden gate
  + Miniprepped more double plasmid system parts
  + Ran a gel on all of our inverse PCR results and picked the best one for gel extraction DNA clean up protocol
  + Performed another inverse PCR on the double plasmid backbone to get rid of the extra bands we’ve been seeing on the gels (increased binding temperature of the primers by a couple degrees)
  + More argonaute primer, optimized CPF1, and optimized GFP dilutions done

## September Overview

In September, our main focus has been on Protocol Troubleshooting and Construction of Plasmids

![flask](https://static.igem.wiki/teams/5149/img-9257.jpeg)

![flask-single](https://static.igem.wiki/teams/5149/img-9624.jpeg)

## Culture Growth

UTEX 3154

![Description image](https://static.igem.wiki/teams/5149/description/des-i.png)

UTEX 3154

![Description image](https://static.igem.wiki/teams/5149/description/des-i.png)

UTEX 2973

![Description image](https://static.igem.wiki/teams/5149/description/des-f.png)

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![home1](https://static.igem.wiki/teams/5149/stickers/bender-sloth-1.png)

Limnospira Fusiformis is a difficult to transform cyanobacteria known for its nutritional content, which our team hoped to leverage to produce
ingredients of premium baby formula. Even as we pivoted to differing strains such as **UTEX 2973, 3154, and PCC 7942,** the problem of transforming polyploid cyanobacteria
proved difficult and time-intensive to solve. To make polyploid cyanobacteria genetically tractable, **we needed to overcome the inherent Restriction Modification systems**. To overcome this immune system, we developed BLACKBIRD to utilize the STEALTH algorithm and remove possible RMs targetted sights from our gene
insert as well adapting the insert to the target’s codon bias. Below we detail the Design-Build-Test-Learn process we undertook on the program to ensure its viability
in engineering.

### Swipe down on each DBTL to see full Design-Build-Test-Learn cycle

![Slide 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-design.png)

**Design:** The tentative designs of the BLACKBIRDCoOp algorithm aimed to develop an application of the Stealth algorithm, to overcome the hurdles of the Restriction
Modification System (RMS). Initially, using the L. Fusiformis genome, the program was to use Stealth to find all potential RMS cut sites, and excise them from the gene insert
provided. We were provided the Stealth outputs for L. Fusiformis and UTEX 2973, which we piped into our program. As we were trained in Python from our coursework, we decided to
implement our solution in it. The pipeline generated operated as such:

| Step | Input | Output |
| --- | --- | --- |
| Insert translation | FASTA sequence | Python string |
| RMS site detection | Python string, List of Strings | List of List Indices |
| RMS site removal | Python string, List of List Indices, List of Strings | Python string |

![Slide 3 - Image 2](https://static.igem.wiki/teams/5149/dbtl/dbtl-build.png)

**Build:** The first iteration of the BLACKBIRDCoOp program set the groundwork for the rest of the iterations. In order to create an automated pipeline,
a FASTA file reader was implemented to read gene inserts provided to BLACKBIRDCoOp, as well as a program to read the Stealth results. After they were 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.

![Slide 3 - Image 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-test.png)

**Test:** The first run of testing utilized the gene sequence of the EGFP protein for BLACKBIRDCoOp, as it would be used as a screening marker
in our wet lab experiments. This testing cycle 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.

![output-1](https://static.igem.wiki/teams/5149/dbtl/dbtlcycle1result.webp)

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.

![Slide 3 - Image 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-learn.png)

**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.

![Slide 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-design.png)

**Design:** During the second cycle, we added a second member to the dry lab team and had more time to design the process. We 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:

| Step | Input | Output |
| --- | --- | --- |
| FASTAReader | FASTA file | Python String |
| STEALTHParser | TXT file | List of Strings |
| codonUsage | Python String | Dictionary of List of Lists |
| StealthHits | Python String, List of Strings | List of Lists |
| codonChoice | List of Lists, Python String, Dictionary of List of Lists | Python String |

![Slide 3 - Image 2](https://static.igem.wiki/teams/5149/dbtl/dbtl-build.png)

**Build:** Various new Python modules and updated names were added to the BLACKBIRDCoOp build. The FASTA parser was named FASTAReader, and a STEALTHParser module
was introduced; it 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 dry lab team to use throughout. This was separate from the 2024 iGEM GitLab, which was
activated later in the process.

![Slide 3 - Image 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-test.png)

**Test:** Testing the new version of BLACKBIRDCoOp 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.

![output-2](https://static.igem.wiki/teams/5149/dbtl/dbtlcycle2result.webp)

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

![Slide 3 - Image 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-learn.png)

**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. Another problem arose where the program possibly created 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.

![Slide 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-design.png)

**Design:** The third cycle of the BLACKBIRDCoOp 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:

| Step | Input | Output |
| --- | --- | --- |
| FASTAReader | FASTA file | Python String |
| STEALTHParser | TXT file | List of Strings |
| codonUsage | Python String | Dictionary of List of Lists |
| StealthHits | Python String, List of Strings | List of Lists |
| codonChoice | List of Lists, Python String, Dictionary of List of Lists | Python String |

![Slide 3 - Image 2](https://static.igem.wiki/teams/5149/dbtl/dbtl-build.png)

**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. [[2][3]](#ref1-eng)
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.

![Slide 3 - Image 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-test.png)

**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.

![Slide 3 - Image 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-learn.png)

**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
this would require a more professional solution in time, as the current solution would not be functional in a package workflow.

![Slide 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-design.png)

**Design:** As we worked on redesigning the editing process of the gene insert, we reconsidered and returned to the drawing board for methods on how to edit
the sequence. The current approach of selecting an optimal codon has resulted in a loss of variation within the gene insert. Meeting with our PI Dr. David L. Bernick,
we learned how variability in our insert would overall make it stronger, and implementing a stochastic process to develop gene inserts would lead to higher
transformation efficiency. For this we began down two design paths concurrently. We designed an editing window approach, which would function by generating a
five codon window at the start of each Stealth site. This would allow newer sites generated to be caught easier. In previous manual testing, we discovered certain
Stealth sites to be immutable with the codons usable, and thus created a forfeit mechanism in which the program would move to the next site if a Stealth site could
not be removed from the current window. This functionality was added to the codonChoice module. To address the issues of variation, an alternate strategy discussed
was the development of a weighted random assignment for the codon replacements.

| Step | Input | Output |
| --- | --- | --- |
| FASTAReader | FASTA file | Python String |
| STEALTHParser | TXT file | List of Strings |
| codonUsage | Python String | Dictionary of List of Lists |
| StealthHits | Python String, List of Strings | List of Lists |
| codonChoice | List of Lists, Python String, Dictionary of List of Lists | Python String |

![Slide 4 - Image 2](https://static.igem.wiki/teams/5149/dbtl/dbtl-build.png)

**Build:**The first step of the new updates required the creation of the editing window that would allow for the detection of newly generated sites with each edit.
The window was set at five codons, as a Stealth site 4 to 6 nucleotides long would be caught by a search in the window. The program would place the starting codon of
the Stealth site as the third codon in the window. Edits would be applied to this codon, and the window would be searched for any newly generated sites. If a site
persisted after using a codon with the matching rank, the next in the ranking would be attempted, and so forth until every codon was attempted. If the site persisted,
the window would shift to the next codon and attempt the cycle again. Lastly, if none of the edits successfully removed the site, the sequence would continue to the
next site leaving the site as it was found.

 A secondary development was the random weighing program. These would randomly assign a codon as before, but utilize a weighted random that would convert the
genome’s codon bias into a representative weight with which to choose the codon. The percent usage of the codon according to the codon bias table would be converted
to a percent to be used for the weighing. This workflow was gradually dropped to be implemented at a later time due to disputes over calculations.

![Slide 4 - Image 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-test.png)

**Test:**In the process of testing, we faced issues with ‘Maximum Recursion Errors’ that occurred as the program functioned recursively and kept calling itself
indefinitely until the program ran out of memory. This was partially remedied by the use of the forfeit mechanism that would simply pass on to the next site, though
a more elegant solution should be devised. However, with the development of this version of BLACKBIRDCoOp, we successfully managed to reduce the Stealth sites down
to below 5% of their original amount. This was very successful, and in line with studies on editing RMS sites, would increase the transformation efficiency significantly.

![Slide 4 - Image 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-learn.png)

**Learn:** BLACKBIRDCoOp, having shown significant results in respect to modifying gene inserts, was close to a finished product at the end of this cycle.
The results from the program were promising. After adapting it for use by the general public, we condensed it into an installable package for use on the Command Line.
We consulted with Reto Stamm and utilized his Python Packaging Index to turn our program into a usable package. Updating the official iGEM gitLab followed,
as we had yet to activate it. As we finished up this version of BLACKBIRDCoOp, we were left with many possible avenues with which to pursue further development.

From the parts of the project we worked on, more accessibility was first considered, as compatibility with more file types would allow for more widespread
adaptation of the program and encourage research on RMS containing organisms. Furthermore, the variation aspect of results could be further improved to provide a
model more accurate to the conditions within an organism. Finally, more functionality to include user-defined sites to remove and avoid would have made the program
more user-friendly.

A conversion into the Julia programming language was considered, leveraging its faster processing and better data handling to greatly speed up the program,
and is currently in development. A secondary suggestion from our PI was the utilization of Genetic Algorithms to develop a more advanced version of this program as well.
BLACKBIRDCoOp is open-source and available for anyone to build and iterate upon.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

### The BLACKBIRD version **'Alpha'** has achieved notable success in designing custom inserts. The program processes an insert sequence and refines it through multiple iterations of STEALTH, aiming to reduce the number of recognition sites utilized by the target organism's native restriction enzymes.

![header](https://static.igem.wiki/teams/5149/stickers/stealth-sloth.png)

BLACKBIRD is a versatile program that can be calibrated for different organisms using a complete strain-specific genome in FASTA
format and a STEALTH software output. The STEALTH [[1]](#ref1-eng) output file contains the k-mers that correspond to hypothetical RM recognition sites; the generated k-mers will be replaced
from the custom insert. During the initial development of BLACKBIRD, we utilized the genome and STEALTH sites generated for the **well-documented
UTEX 2973** strain. Using these specifications, we customized our three potential inserts accordingly: **GFP, CbAgo**
and **Cas12a**.

![BLACKBIRD Process](https://static.igem.wiki/teams/5149/results/blackbird-result.png)

This process was then replicated for UTEX 3154, our target organism. Since our target organism's genome has not been officially
sequenced, BLACKBIRD currently operates using the PCC 11901 genome, which is closely related to UTEX 3154. The outcomes of BLACKBIRD for
**both UTEX 2973 and PCC 11901 are shown** in the bar graph.

The number of STEALTH hits after a comparable number of iterations through the program for inserts customized to PCC11901 seem to still contain almost 30% of their
initial number of hits whereas that number is closer to 5% when customized to UTEX 2973. Within the current version of BLACKBIRD, this result can be accounted for
by the fact that the number of STEALTH hits for UTEX2973 and PCC11901 are 283 and 754 respectively.

The STEALTH list of k-mers are generated based on a user-determined cut-off value. We initially ran BLACKBIRD on a False
Discovery Rate score (or bootstrap score) of around 72 as our cut-off. Upon discovering the unreliability of the results, this bootstrap score was raised to 86. Because
it is less lenient when generating the list of k-mers, the overall conditions make for better BLACKBIRD results. The right graph in figure shows
the results of moving this cut-off; in one example, **we were able to reduce the number of hits of the PCC11901 customized GFP from 41 to a mere 6**. This was a very
predictable result due to the fact that the number of hits for the higher bootstrap score was similar to that of UTEX2973 for all inserts due to the fact that the
number of STEALTH hits for both conditions was both around 280.

## References

[[1]](#ref1-eng)S. Hu, S. Giacopazzi, R. Modlin, K. Karplus, D. L. Bernick, and K. M. Ottemann, “Altering under-represented DNA sequences elevates bacterial transformation efficiency,” mBio, vol. 14, no. 6, Oct. 2023, doi: https://doi.org/10.1128/mbio.02105-23.

[[2]](#ref2-eng)G. Zhang, "Transient ribosomal attenuation coordinates protein synthesis and co-translational folding" Nat Struct Mol Biol, Jul. 13, 2008 https://www.nature.com/articles/nsmb.1554 (accessed Sep. 26, 2024)

[[3]](#ref3-eng)G. L. Rosano, "Rare codon content affects the solubility of recombinant proteins in a codon bias-adjusted Escherichia coli strain" Microb Cell Fact, Jul. 24, 2009 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723077/ (accessed Sep. 26, 2024).

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block page\_content %}

![Loading...](https://static.igem.wiki/teams/5149/menu/img-1412.gif)

![experiments header](https://static.igem.wiki/teams/5149/headers/experiments-header.png)

### Swipe down on each DBTL to see full Design-Build-Test-Learn cycle

![Slide 1](https://static.igem.wiki/teams/5149/dbtl/dbtl-design-gga.png)

**Design:** We derived our backbone from the pICH41308 plasmid from the MoClo Toolkit [[1][2]](#ref1-exp). pICH41308 is a Level 0 cloning vector with a length of 2247
base pairs that contains LacZ alpha and an antibiotic resistance to spectinomycin. At first, the experiment was conducted using un-optimized inserts;
we later transitioned to using codon-optimized inserts. All of these inserts are considered Level -1 parts and contain BbsI restriction enzyme sites.

![Slide 1 - Image 2](https://static.igem.wiki/teams/5149/dbtl/dbtl-build-gga.png)

**Build** In order to integrate these inserts into the MoClo backbone, we utilized Golden Gate Assembly. Golden Gate Assembly (GGA) is the method by which
one or more inserts can be efficiently integrated into a vector backbone by using a Type IIS restriction enzyme and a T4 DNA ligase in a one-pot reaction.
The Type IIS restriction enzyme in particular has the ability to cleave DNA outside of its recognition sites, which ultimately results in the enzyme's
removal of the recognition sequence. This method is advantageous because of the fact that scar sequences are not introduced, multiple fragments can be integrated
into the backbone, and the digestion and ligation processes can be performed simultaneously.

![Slide 1 - Image 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-test-gga.png)

**Test:** The efficacy of our GGA reaction was tested through multiple steps: electroporation and touchdown PCR. We initially used electroporation in order to transform
our assembled plasmid into electrocompetent E.coli Top10 cells. Following electroporation, we used X-Gal plates to do blue-white screening for transformed colonies.
The idea was that a colony would turn blue if it still contained the LacZ fragment in its genome; if a colony was blue, that would indicate that it was not
transformed with the plasmid due to the fact that the insert should, hypothetically, knock out the LacZ fragment. If a colony turned white, that would indicate
that the colony did not contain the LacZ fragment and was hence transformed with the plasmid.

![Slide 1 - Image 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-learn-gga.png)

**Learn:** Following the transformation of Top10 E.coli cells, we performed
touchdown PCR to see if our GGA reaction worked by confirming the size of our parts, run with a subsequent gel electrophoresis. After testing our GGA, we came
to the consensus that our reaction was unsuccessful, but we decided to troubleshoot the testing steps first. The first conclusion we came to was that electroporation
was unsuccessful due to the fact that the restriction enzymes from the GGA led to high salt concentrations that would cause the E.coli cells to be unable to uptake
the plasmid; this led us to switch to chemi-competent transformation.

![Slide 2](https://static.igem.wiki/teams/5149/dbtl/dbtl-design-gga.png)

**Design:** As mentioned before, the high salt concentration led us to switch to chemi-competent transformation. Another area of concern was our PCR protocol:
previously, we had been following the Q5 protocol[[9]](#ref1-exp) using colonies from the transformed plate, whereas we should have been following the OneTaq protocol[[10]](#ref1-exp) where we
were doing colony PCR. Another limitation we had realized was regarding the blue-white screening process, which could have been potentially unreliable due to the
fact that we were almost exclusively getting white (transformed) colonies. Due to this, we decided to pick ten individual colonies and screen all of them during our PCR process; this was running on the assumption that only some of the colonies were truly transformed with our level 0 part.

![Slide 2 - Image 2](https://static.igem.wiki/teams/5149/dbtl/dbtl-build-gga.png)

**Build and Test:** After troubleshooting all of the testing steps following our GGA reaction, we ran a gel electrophoresis. A successful GGA reaction was indicated by an
absence of a band around 300 base pairs; since that is the size of the region between the flanking primers in lieu of the LacZ fragment. The gel that we ran showed a
band around 300 base pairs, which suggested an unsuccessful GGA reaction due to the fact that the LacZ fragment was removed from the backbone, but the insert was not
integrated into the vector.

![Slide 2 - Image 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-test-gga.png)

**Build and Test:** After troubleshooting all of the testing steps following our GGA reaction, we ran a gel electrophoresis. A successful GGA reaction was indicated by an
absence of a band around 300 base pairs; since that is the size of the region between the flanking primers in lieu of the LacZ fragment. The gel that we ran showed a
band around 300 base pairs, which suggested an unsuccessful GGA reaction due to the fact that the LacZ fragment was removed from the backbone, but the insert was not
integrated into the vector.

![Slide 2 - Image 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-learn-gga.png)

**Learn:** From this, we concluded that our reagents could potentially be an impedance to us having a successful GGA reaction, and that our GGA reaction
itself needed to undergo troubleshooting.

![Slide 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-design-gga.png)

**Design:** In order to troubleshoot our GGA reaction and the reagents we were using, we set up a three-part control experiment with the following design:

**Control 1:** pICH41308 + GFP + T4 Ligase Buffer + DI H2O

**Control 2:** pICH41308 + GFP + T4 Ligase Buffer + Bbs1 Restriction Enzyme + DI H2O (no T4 Ligase)

**Control 3:** pICH41308 + GFP + T4 Ligase Buffer + Bbs1 Restriction Enzyme + T4 Ligase Buffer + DI H2O

Using this control experiment, (1) and (3) should hypothetically work when plated; (1) was the plasmid and (3) was the full integrated plasmid.
(2) should not have worked because it only contained digested plasmid that had not been ligated using the T4 Ligase.

![Slide 3 - Image 2](https://static.igem.wiki/teams/5149/dbtl/dbtl-build-gga.png)

**Build:** After running the troubleshooting experiment, it was concluded that the Bbs1 restriction enzyme stock we were using was defective.
After running a GGA reaction, all three parts of the experiment were plated on X-Gal + Spectinomycin after undergoing a chemi-competent transformation
into DH5-alpha E.coli cells, after discovering that Top10 cells inherently contained a resistance to spectinomycin.

![Slide 3 - Image 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-test-gga.png)

**Test:** The plate for (1) was successful, as it only contained the untransformed backbone; the plates for (2)
and (3) were unsuccessful as the digestion step was not able to occur due to the ineffectiveness of the Bbs1 restriction enzyme.

![Slide 3 - Image 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-learn-gga.png)

**Learn:** From here, we were able to pinpoint the troubleshooting areas for Golden Gate, and could now have the potential to successfully
create a level 0 part, with the goal of eventually being able to create a level T construct.

![Slide 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-design-gga.png)

**Design:** After much troubleshooting with the GGA itself and every step of the testing process, we were able to develop a
steady pipeline for being able to develop level 0 parts with the goal of eventually creating a level T construct.

![Slide 4 - Image 2](https://static.igem.wiki/teams/5149/dbtl/dbtl-build-gga.png)

**Build:** The final design are as listed below:

**GGA:** integrate optimized level -1 inserts with Bbs1 restriction sites into pICH41308 MoClo backbone

**Transformation:** transform chemi-competent DH5-alpha cells with GGA product; plate and incubate overnight using blue-white screening.

**PCR:** pick ten different transformed white colonies from plates and dilute in 10 μL DI H2O; follow the NEB protocol using OneTaq 2X MasterMix,
adjust annealing temperature and extension time in accordance with the insert present in the amplicon.

**Gel:** run the PCR samples on a 1% agarose gel and observe the size of the bands.

![Slide 4 - Image 3](https://static.igem.wiki/teams/5149/dbtl/dbtl-test-gga.png)

**Test:** With this new pipeline in mind, we tested using codon-optimized inserts of Cpf1, GFP, CbAgo, and more (for all of the parts,
look at the [Contribution](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27contribution%27%29%20%7D%7D) page!). Throughout our testing process, we were able to prove that we had successful GGA reactions for codon-optimized Cpf1,
GFP, and CbAgo. In addition to having these level 0 reactions, we were also able to ligate a level 1 antibiotic-resistance construct with success
using the appropriate parts from the CyanoGate Toolkit.[[8]](#ref1-exp)

![Slide 4 - Image 4](https://static.igem.wiki/teams/5149/dbtl/dbtl-learn-gga.png)

**Learn:** In order to confirm the identity of the level 0 parts that were isolated, we sent many of our samples to UC Berkeley for sequencing.

Iteration 1
Iteration 2
Iteration 3
Iteration 4

## Protocols

The following is the most up-to-date set of protocols we implemented throughout the project.
Each protocol section contains 3 sections: Materials, Protocol, and Troubleshooting Tips. It's important to highlight that many of these protocols
were specifically designed for troubleshooting purposes. Some were developed in response to challenges encountered during the project, while others were
fine-tuned to ensure smooth operations. These protocols reflect the evolving nature of our approach and were essential in addressing and mitigating any
unforeseen issues along the way.

## Miniprep Protocol (Adapted from Zymo Zyppy Plasmid Miniprep Kit Protocol[[3]](#ref1-exp))

##### Materials

* Sample DNA
* Zymo Zyppy Plasmid Miniprep Kit (Zymo Research D4036)

##### Protocol

**Note:** Make sure to time all of your steps where the timing matters!

1. Spin down 5mL of culture in large centrifuge at 2000 rcf for 5 minutes
2. Dump out supernatant, cut the pipet tip & resuspend pellet in 600uL of DI water
3. Transfer sample to 1.5mL microcentrifuge tube
4. To sample, add 100uL of 7X Lysis Buffer (blue) and mix by inverting the tube 4-6 times
   * Proceed to step 5 within 2 minutes (opaque/clear blue = lysis indicator)
5. Add 350uL of Cold Neutralization Buffer (yellow) and mix thoroughly. Sample will turn yellow when neutralization is complete and yellowish precipitation will form. Invert the sample an additional 2-3 times to ensure complete neutralization
   * Wait until about 1 minute 50 seconds to add Neutralization buffer
   * Only invert until the blue color goes away and no more
6. Centrifuge at 11000-16000 rcf for 6 minutes in small centrifuge
7. Transfer the supernatant (~800uL – don't be greedy!!) into the Zymo-Spin In column, avoid disturbing the cell debris pellet
8. Place the column into a collection tube and centrifuge for 30 seconds
   * Empty the collection tube after each spin down
9. Discard the flow through and place the column back into the same collection tube
10. Add 200uL of Endo-Wash Buffer to the column, centrifuge for 30 seconds
11. Add 400uL of Membrane Wash Buffer to the column, centrifuge for 1 minute
    * Repeat this step twice
    * Then do one more spin down without anything in it
12. Transfer the column into a clean 1.5mL microcentrifuge tube, then add 30uL DI water directly to the column and let it stand more 1 minute at room temperature
13. Centrifuge for 30 seconds to elute the plasmid DNA
14. Nanodrop to verify results

##### Troubleshooting Tips

* Do not spin too fast for the initial spin
  + We spin at 2000 rcf for 6 mins
* Check reagents to ensure they are still good
* Choose single colonies
* Spin down in the 15 ml tubes
* We made our own membrane wash buffer
* Perform 2 membrane wash steps
* Spin 1 more time after membrane wash to get rid of any leftover ethanol
* MAKE SURE NEUTRALIZATION BUFFER IS PUT IN FRIDGE

## Ethanol Precipitation of DNA Protocol

##### Materials

* Sample DNA
* 100% EtOh
* 3M sodium acetate
* 75% EtOH
* MilliQ water

##### Protocol

1. Starting with ~58μL sample
   * Add 2.5-3.0 volumes ice cold 100% ethanol
   * Add 0.1 volumes 3M sodium acetate
2. Vortex to mix
3. Precipitate overnight at -20℃
4. Centrifuge at full speed (16000 rpm) for 30 minutes, at 4℃
5. Wash pellet twice with 500μL ice cold 75% ethanol
   * Spin at 4℃ for 10 minutes
6. Take ethanol out, spin at full speed to remove trace amounts
7. Air dry & resuspend in 25μL nuclease free water

##### Troubleshooting Tips

* Leave DNA mixed with sodium acetate and EtOH overnight for better results
* Make sure you have the hinges facing outwards when you centrifuge so that you know where the DNA will collect if a pellet is not present

## Genomic DNA Extraction Protocol (Adapted from Zymo Quick-DNA HMW MagBead Protocol + 2023 iGEM TABI Protocol [[4][5]](#ref1-exp))

##### Materials

* Sample DNA
* Zymo Quick DNA HMW Magbead Kit (Zymo Research D6060)
* 70% EtOH
* MilliQ Water
* Shaker

##### Protocol

**Note:** Do not pipet to resuspend !

1. Add 400uL (equal volume) Quick-DNA MagBinding Buffer to 400uL of the sample
2. Resuspend by flicking the tube or dragging it across the microcentrifuge tray
3. Add the 33uL of MagBinding Beads to each sample
4. Resuspend by flicking the tube or dragging it across the microcentrifuge tray and place the sample on a shaker for 10 minutes (speed 3-4)
5. Transfer the sample to the magnetic stand until beads have separated from the solution, then remove & discard the supernatant, transfer the sample off of the magnetic stand
6. Add 500uL Quick-DNA MagBinding Buffer
7. Resuspend the beads by flicking the tube or dragging it across the microcentrifuge tray, then place the sample on shaker for 5 minutes
8. Transfer the sample to the magnetic stand until the beads have separated from solution, then remove and discard the supernatant, transfer the sample off of the magnetic stand
9. Add 500uL DNA Pre-Wash Buffer
10. Resuspend the beads by flicking the tube or dragging it across the microcentrifuge tray
11. Transfer the sample to the magnetic stand until the beads have separated from solution, then remove and discard the supernatant, transfer the sample off of the magnetic stand
12. Add 900uL 70% EtOH & resuspend the beads by flicking the tube or dragging it across the microcentrifuge tray
13. Transfer all liquid to a new microcentrifuge tube
14. Transfer the sample to magnetic stand until beads separated from solution, remove and discard the supernatant, transfer sample off of magnetic strand
15. Repeat steps 12-14
16. To dry the beads, transfer the sample to 55℃ incubator & incubate for 10 minutes (or let air dry for 20 minutes)
17. Add 50uL of DI water to each sample
18. Put sample on shaker for 5 minutes at room temperature
19. Transfer the sample to a magnetic stand until beads have separated from solution, then transfer the eluted DNA to a new tube
20. Nanodrop
21. Run a gel to verify results

##### Troubleshooting Tips

* Let samples sit in DNA/RNA shield for 1 hour at room temperature
* Add proteinase K and incubate for 2 hours at 55°C
* Add RNAse A and incubate at room temperature for 30 mins
* Instead of g-DNA wash buffer, use 70% EtOH
* NO PIPETTE MIXING

## Gel electrophoresis Protocol

##### Materials

* Agarose powder
* 1xTBE
* Sample DNA
* Gel Dye
* Gel Box
* MilliQ Water

##### Protocol

1. Measure 0.8g-2g of agarose depending on the fragment size intended to be resolved
2. Mix agarose powder with 100 mL 1xTBE in a 200mL microwavable flask
3. Microwave for 1-3 min until the agarose is completely dissolved
4. Let the agarose solution cool down, add 5 ul of gel dye and cool to about 50 °C for about 5 mins
5. Pour the agarose into a gel tray with the well comb in place
6. Let newly poured gel sit at room temperature for 20-30 mins, until it has completely solidified
7. Fill the gel box with 0.5x TBE until the gel is covered
8. Carefully load a molecular weight ladder into the first lane of the gel
9. Carefully load your samples into the additional wells of the gel
10. Run the gel at 80-150 V until the dye line is approximately 75-80% of the way down the gel
    * A typical run time is 1-1.5 hours, depending on the gel concentration and voltage
11. Visualize results

##### Troubleshooting Tips

* Make sure you microwave the TBE and agarose until agarose is fully dissolved into the solution
* Microwave in intervals, we did 1 min, 20 sec., and then 10 sec until fully dissolved

+ Wait around 15 sec. between microwave intervals, or the solution could boil over

* Use more loading dye to ensure your samples visualize on the gel

+ We went from 1µL to 1.5 µL

* DO NOT FORGET TO ADD GEL STAIN

+ And don't forget to put it back in the fridge when done

* Remember to use the same TBE buffer you use to make the gel to run the gel

## Golden Gate Assembly Protocol (Adapted from NEB’s Golden Gate Assembly Protocol [[6]](#ref1-exp))

##### Materials

* Thermocycler
* BbsI-HF or Bsa1-HFv2
* T4 DNA Ligase
* 10x T4 DNA Ligase Buffer
* Destination plasmid
* Insert
* MilliQ Water

##### Protocol

1. For each tube, prepare the following reaction:

   | Reagent | Volume (μL) |
   | --- | --- |
   | BbsI-HF or Bsa1-HFv2 | 1.5 |
   | T4 DNA Ligase | 0.5 |
   | 10x T4 DNA Ligase Buffer | 2.5 |
   | Destination plasmid | 1 |
   | Insert | 0.75 per insert |
   | Milli Q H2O | Up to 19.5 |
   | **Total:** | **25 μL** |
2. Place sample in the thermocycler with the following steps:

   | Step | Temperature (℃) | Time (min) |
   | --- | --- | --- |
   | 1 | 37 | 5 |
   | 2 (30x cycles) | 16 | 5 |
   | 3 | 60 | 5 |
   | Hold | 4 | ∞ |
3. If done overnight, hold the protocol at 4℃, then do step 3 the following day prior to transformation.
4. Run a transformation with chemi-competent E. coli Top10 cells subsequent to Golden Gate Assembly

##### Troubleshooting Tips

* To verify golden gate products, design primers that target the ligation sites
* Increase the molar ratio of insert to backbone from 2:1 to 3:1 / 4:1 to ensure the inserts ligates into the backbone
* C1V1 = C2V2 is your best friend
* If your parts contain a LacZ fragment, it is helpful to make x-gal plates to test if your insert actually ligated to your backbone

## Chemi-competent E. coli Transformation with Blue-White Screening Protocol

##### Materials

* SOC Medium
* E. coli cells (previously made chemi-competent)
* Golden Gate Assembly Plasmid Product
* Hot Water Bath
* Incubator
* LB+X-Gal+IPTG+Spec Plates

##### Protocol

1. Take competent cells out of -80℃ and thaw on ice until no longer frozen.
2. Mix 1-5 μL of plasmid product into 50 μL of competent cells (aim for 10 ng of plasmid for 50 μL of competent cells). Resuspend using a pipet (cut the tip in order to avoid lysing the cells).
3. Incubate the transformation tube on ice for 30 minutes.
4. Heat shock the transformation tube by placing it in a hot water bath heated to 42 ℃ for 90 seconds.
5. Immediately place the tube on ice for 1 minute.
6. Add 800 μL of SOC medium immediately after as a recovery step.
7. Place the tube in a shaking incubator for 45 minutes, under 37 ℃ and 225rpm.
8. Take the liquid culture out of the incubator and plate using 25-50 μL of liquid culture. Incubate the plates overnight for 12-16 hours.
9. Once taken out of the incubator, look for white colonies in order to select for those which are transformed (blue colonies indicate untransformed colonies).

##### Troubleshooting Tips

* Know the genotype of your competent cells

+ For instance Top10 are naturally resistant to Streptomycin, so a Strep plate would not be good for selection

## Touchdown Colony PCR of Construct in E. coli Protocol

##### Materials

* Thermocycler
* OneTaq 2x Master Mix with Standard Buffer (NEB)
* X-Gal plates containing transformed E. coli colonies
* Primer (2.5 μM)
* Reverse Primer (2.5 μM)
* Template DNA
* MilliQ Water

##### Protocol

1. Take plates with grown, transformed colonies and pick 1 colony per 25 μL PCR reaction.
2. Set up the following PCR reaction:

   | Reagents | Volume (μL) |
   | --- | --- |
   | Forward Primer (2.5 μM) | 2 |
   | Reverse Primer (2.5 μM) | 2 |
   | Transformed Colony Sample | 1 |
   | OneTaq 2x Master Mix | 12.5 |
   | MiliQ Water | 7.5 |
   | **Total:** | **25 μL** |
3. Program the thermocycler with the following conditions:

   | Step | Temperature (℃) | Time (seconds) |
   | --- | --- | --- |
   | Denaturing | 94 | 30 |
   | Annealing | 51 | 45 |
   | Extension | 68 | 1 min per kb |

   Repeat the steps below for 20 cycles, while setting up a gradient for the annealing step.

   | Step | Temperature (℃) | Time (seconds) |
   | --- | --- | --- |
   | Denaturing | 94 | 30 |
   | Annealing | 48-53 | 45 |
   | Extension | 68 | 1 min per kb |

   Program the following steps for safety.

   | Step | Temperature (℃) | Time (minutes) |
   | --- | --- | --- |
   | Final Extension | 68 | 5 |
   | Hold | 4 | ∞ |
4. Perform a gel electrophoresis in order to visualize the results of the colony PCR

##### Troubleshooting Tips

* Make sure primers are diluted to correct molarity

+ Usual molarity is 10 mM, but we adjusted to 2.5 mM to get a more accurate volume

* Make a master mix first containing your buffer and DNA polymerase
* Aliquot out your master mix to prevent freeze thaw cycles

+ We had a final volume of 25 µL and we had 2x mastermix, so we aliquoted out 12.5 master mix

* Calculate out your primers annealing temp

+ NEB Tm calculator is a big help

* Program the right extension time in your thermocycler based on sequence length, buffer, and enzyme

+ 30 secs/kb for Q5 and 1 min/kb for OneTaq

## Touchdown PCR using Q5 High-Fidelity 2X Master Mix Protocol

##### Materials

* Thermocycler
* Q5 High-Fidelity 2X Master Mix
* Forward Primer (10 μM)
* Reverse Primer (10 μM)
* Template DNA
* MilliQ Water

##### Protocol

1. Set up the following reactions (for 25 μL reaction):

   | Reagents | Volume (μL) |
   | --- | --- |
   | Q5 High-Fidelity 2X Master Mix | 12.5 |
   | Forward Primer (10 μM) | 1.25 |
   | Reverse Primer (10 μM) | 1.25 |
   | Template DNA | Variable |
   | MilliQ Water | Up to 25 μL |
   | **Total:** | **25 μL** |
2. Program the thermocycler using the following conditions:

   | Step | Temperature (°C) | Time (seconds) |
   | --- | --- | --- |
   | Initial Denaturation | 98 | 30 |
   | 25 cycles | 98 | 5-10 |
   |  | 50-72 | 10-30 |
   |  | 72 | 30 seconds per 1kb |
   | Final Extension | 72 | 2 minutes |
   | Hold | 4 | ∞ |
3. Run a subsequent gel electrophoresis in order to confirm presence of the construct

##### Troubleshooting Tips

* Dilute one colony in 10 µL of DI water to not overload PCR with too much template DNA, otherwise the same as PCR

## DNA Gel Extraction Protocol (Adapted from NEB Monarch® DNA Gel Extraction Protocol[[7]](#ref1-exp))

##### Materials

* NEB Monarch® DNA Gel Extraction Kit (NEB #T1120)

##### Protocol

**Note:** All centrifugation steps performed at 16000g.

1. Excise the DNA fragment from the gel using a razor blade, weigh the sample, and transfer the sample to a 1.5mL microcentrifuge tube
2. Add 4 volumes of Monarch Gel Dissolving Buffer to the tube with the gel (ie. 400μL for 100 mg agarose)
   * If gel slice > 150mg, consider decreasing the Gel Dissolving Buffer to 3 or 3.5 volumes
3. Incubate the sample between 37-55℃ (typically 50℃), inverting periodically until the gel slice is completely dissolved (often 5-10 minutes)
4. Insert the column into a collection tube and load the sample onto the column, spin for 1 minute, then discard the flow-through
5. Re-insert the column into the collection tube, add 200μL DNA Wash Buffer, and spin for 1 minute (discarding flow-through is optional)
6. Repeat wash (Step 5)
7. Transfer the column to a clean 1.5 microcentrifuge tube, make sure the tip of the column doesn't come into contact with the flow-through, but if in doubt, re-spin for 1 minute before placing the column into a clean microcentrifuge tube
8. Add ≥ 6μL DNA Elution Buffer, let stand for 1 minute, and then spin for 1 minute to elute the DNA

## General Culturing Protocol

##### Materials

* Strain of choice
* Growing medium of choice (LB+Amp, LB+Kan, or LB+Spec for E.coli; BG-11 or SOT for Cyanobacteria)

+ BG-11 media (recipe described below)

  | Stocks | Per 500mL / 100mL / Liter |
  | --- | --- |
  | NaNO3 | 75.0g (per 500mL) |
  | K2HPO4 | 4.0g (per 100mL) |
  | MgSO4·7H2O | 7.5g (per 100mL) |
  | CaCl2·2H2O | 3.6g (per 100mL) |
  | Citric Acid\* | 0.60g (per 100mL) |
  | Ammonium ferric citrate green\* | 0.60g (per 100mL) |
  | Na2EDTA | 0.10g (per 100mL) |
  | Na2CO3 | 2.0g (per 100mL) |
  | **Trace elements:** | |
  | H3BO3 | 2.86g (per Liter) |
  | MnCl2·4H2O | 1.81g (per Liter) |
  | ZnSO4·7H2O | 0.22g (per Liter) |
  | Na2MoO4·2H2O | 0.39g (per Liter) |
  | CuSO4·5H2O | 0.08g (per Liter) |
  | Co(NO3)2·6H2O | 0.05g (per Liter) |

  | Medium | Per Liter |
  | --- | --- |
  | Stock solution 1 | 10.0mL |
  | Stock solutions 2-9 | 1.0mL each |

  - Make up to 1 Liter with deionized water. Adjust pH to 9.0 with 1M NaOH or HCl. For agar, add 15.0g per liter of Bacteriological Agar. Autoclave at 15 psi for 15 minutes.
  - Due to precipitation, larger volumes require stocks 5 & 6 to be autoclaved separately or filter sterilized and added to the sterile medium in the airflow cabinet.
  - For nitrogen-fixing cyanobacteria, prepare standard BG-11 while omitting Stock 1

##### Protocol

1. Add 35mL of BG-11 to a cell culture-treated flask
2. Do a 3% passage: add ~1.0mL of cyanobacteria sample to the flask
3. Incubate at 38℃ and 60 rpm, set the lights inside to a 12hr on/off cycle
4. Check the OD’s every 12 hours (ie. 9am & 9pm)
5. Depending on the OD, do 3-10% passages every few days

##### Troubleshooting Tips

* Let grow overnight
* For E.coli

+ Take OD’s and mini prep after it reaches OD600 of >0.7
+ When taking OD’s, fill cuvette to 850 µL

* For UTEX3154

+ Switch to pasteur pipettes as opposed to micro pipettes to ensure sterility
+ Increase the inoculant ratios

- Went from 3% inoculation to more than 5%

+ When taking OD’s, fill cuvette to 600 µL to reduce volume lost in the culture
+ We set the light at 333 µmol/s/m2
+ Warm up BG-11 before making inoculants
+ Optimal media is not always necessary

- BG-11 is not the optimal media, but they grew just fine

## References

[[1]](#ref1-exp) A modular cloning system for standardized assembly of multigene constructs. Weber E, Engler C,
Gruetzner R, Werner S, Marillonnet S. PLOS ONE . 2011 Feb 18;6(2):e16765. doi: 10.1371/journal.pone.0016765. PubMed PMID 21364738.

[[2]](#ref2-exp) Fast track assembly of multigene constructs using Golden Gate cloning and the MoClo system. Werner S,
Engler C, Weber E, Gruetzner R, Marillonnet S. Bioeng Bugs. 2012 Jan 1;3(1):38-43. doi: 10.1371/journal.pone.0016765. PubMed PMID 22126803.

[[3]](#ref3-exp) “Zyppy Plasmid Miniprep Kit,” ZYMO RESEARCH, 2023.
https://www.zymoresearch.com/products/zyppy-plasmid-miniprep-kit?srsltid=AfmBOopa0khOS21ey9b1XdMteeDLi8\_AkEL4B1AiXEzgxBQaVL6doKVe (accessed Oct. 01, 2024).

[[4]](#ref4-exp) “Quick-DNA HMW MagBead Kit,” ZYMO RESEARCH, 2023.
https://www.zymoresearch.com/products/quick-dna-hmw-magbead-kit?srsltid=AfmBOoo7CSVk-ji8O-e59cGYkvv6pm8aJsABiL7Xd419gHGh9WfpLeyo (accessed Oct. 01, 2024).

[[5]](#ref5-exp) “| UCSC - iGEM 2023,” Igem.wiki, 2023. https://2023.igem.wiki/ucsc/ (accessed Oct. 01, 2024).

[[6]](#ref6-exp) New England Biolabs, Neb.com, 2024.
https://www.neb.com/en-us/protocols/2015/03/04/golden-gate-assembly-protocol-for-using-neb-golden-gate-assembly-mix-e1600 (accessed Oct. 01, 2024).

[[7]](#ref7-exp) New England Biolabs, Neb.com, 2024. https://www.neb.com/en-us/products/t1020-monarch-dna-gel-extraction-kit (accessed Oct. 01, 2024).

[[8]](#ref8-exp)CyanoGate: A Modular Cloning Suite for Engineering Cyanobacteria Based on the Plant MoClo Syntax. Vasudevan R, Gale GAR,
Schiavon AA, Puzorjov A, Malin J, Gillespie MD, Vavitsas K, Zulkower V, Wang B, Howe CJ, Lea-Smith DJ, McCormick AJ. Plant Physiology.2019 May;180(1):39-55.
doi: 10.1104/pp.18.01401. PubMed PMID: 30819783.

[[9]](#ref9-exp)
New England Biolabs, Neb.com, 2024. https://www.neb.com/en-us/protocols/2012/12/07/protocol-for-q5-high-fidelity-2x-master-mix-m0492 (accessed Oct. 01, 2024).

[[10]](#ref10-exp)
New England Biolabs, Neb.com, 2024. https://www.neb.com/en-us/protocols/2012/09/06/protocol-for-onetaq-2x-master-mix-with-standard-buffer-m0482 (accessed Oct. 01, 2024).

{% endblock %}

{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block page\_content %}

![Loading...](https://static.igem.wiki/teams/5149/menu/img-1412.gif)

![results header](https://static.igem.wiki/teams/5149/headers/results-header.png)

![header](https://static.igem.wiki/teams/5149/results/results-figure1.png)

### Our primary objective was to improve the efficiency of genetic transformation in cyanobacteria. These organisms are polyploid, meaning they possess multiple sets of chromosomes, unlike humans, which are predominantly diploid.

### Existing cyanobacterial transformation methods rely heavily on chance and the cell's natural homologous recombination repair mechanisms. The process involves creating a break in the DNA at the desired insertion site, hoping that the introduced genetic material will integrate into the genome. However, this approach lacks precision and can be time-consuming due to the random nature of the DNA breaks.

![header](https://static.igem.wiki/teams/5149/results/results-doodle.png)

### To demonstrate the effectiveness of our "accelerator" tool, we will measure the GFP fluorescence (normalized to cell count) at 680 nm in cyanobacterial cultures both with and without our accelerators. From this experiment we expect three possible results:

![header](https://static.igem.wiki/teams/5149/results/results-figure3.png)

![header](https://static.igem.wiki/teams/5149/results/results-figure2.png)

### Ideally, we would observe a left-shifted curve, similar to the **green** one, in our cyanobacterial cultures with accelerators. This would indicate the successful acceleration of stable transformant generation. However, if the curve resembles either of the **red** curves, it would suggest that our accelerators are not functioning as intended.

## References

[[1]](#ref1-results) Figures (by appearance) 1, 3, 4 made in BioRender. “Scientific Image and Illustration Software | BioRender,” Biorender.com, 2024. https://www.biorender.com/

{% endblock %}