Team:Purdue/Engineering

Purdue iGEM Page

Engineering Design


Biologics


The Basis of cArgo's Biological System

The biologics of our device are based off the PAND or PfAgo mediated nucleic acid system from literature[1]. We decided to use the argonaute TtAgo for our detection based on its availability and its reaction temperature between 65C-85C. The basic principle of our biological detection system is summarized in the figure from literature[1] below:


Molecular Beacon Design

In order to easily detect the presence of our RNA fragment of interest, we needed to use a probe that will emit a visible signal with high specificity to the molecule of interest. For our device cArgo, we decided to use a molecular beacon probe specific to the TtAgo guide DNA produced after Argonaute cleavage. Currently available software for the design of molecular beacons are often expensive and inaccessible, hence we decided to develop our own molecular beacon design software. Literature [2] suggested the G-C content of the stem be between 70-80% and to avoid sequences with a 5’ guanine.
Our initial approach was taking a specific probe sequence from the COVID genome. It was then screened for all possible stems according to the guidelines outlined above. The probe and stem combined sequence was uploaded to Quikfold to determine sequences which folded into the correct hairpin structures. From the sequences that did fold into hairpin structures, we manually screened the results to identify any inconsistencies with the outlined parameter.

Scaling Up

Given that our initial approach required a considerable amount of tedious data entry into various websites by the user, in order to construct a more robust and scalable code, we implemented web scraping in Python by using the Selenium library. Through this library, we were able to automate the interaction of primer3 and Quikfold to eliminate the user’s need to act as a middle point.

First Iteration

The first code generated a list of best molecular beacons but it was limited in that it could only do so for beacons with a stem length of 5 bp. As an input, the code took a DNA sequence (5’ to 3’). Example input below:

Its output was a list of sequences of molecular beacons 5’->3’.


Further Optimization

After further literature review, it was clear the stem length needed some flexibility as it could vary between 4-7 nucleotides [3,4,5]. Hence, we optimized our code to be even broader by allowing stem lengths between 4-7 nt. We also reformatted it to store information as classes improving computational efficiency; We determined a more objective measurement of adequacy of the beacon to be Gibbs free energy and hence we decided to sort the best beacons by increasing ∆G. An example output is shown below for the same sequence:

Target And Guide DNA Identification

In order to determine which sequence in the genome lend itself better to hybridization by the TtAgo protein, we designed guide nucleotides (gt, gr, gn, gf) which met the following characteristics as outlined in literature [6,7] (for all positions, genome is being read 5’ to 3’):

  • For gn:
    • First position starts with T
    • 12th position is A
  • For gf:
    • Position 10 nt upstream of 5' start of gn is a T
    • 12th position is G or C
  • For gt:
    • From 3' end of gn, 10 nt upstream is a T
    • 12th position is G or C
  • For gr:
    • Base pair 10 nt upstream of 3' end of gn is a T
    • 12th position is G or C
    • GC content between 12-38%

Example guide DNA sequences:

(T in first position is in red and G/C in 12th position is in green).

Primer Design

We designed RPA primers following recommendations from TwistDx and utilizing the open-source software Primer3 under the following conditions:

  • Primer length: 30-36bp
  • GC content: 30-70%
  • Melting temperature: 50-100 °C
  • Product range: 100-200 bp
  • Maximum repeat: 5 bp

Initially, we searched current literature and used Basic Local Alignment Search Tool (BLAST) to identify unique sequences specific to Sars-CoV-2. However, further automation was pursued to identify a specific target compatible for Argonaut detection assays.

Integrated Code

We combined all three essential aspects of our design: identification of a target and generation of appropriate guide DNA sequences, design of primers suitable for Recombinase Polymerase Amplification of the previously identified target, and design and classification of compatible molecular beacon probes. We found the use of .txt files saved computational power considerably from other existing software [5] and prevented having to run the same sequence multiple times. Our final code takes as an input a .txt file containing a whole-genome sequence of an organism of interest (entered in 5’ to 3’ direction) and returns: the most suitable target for a probe sequence, a .txt file containing RPA amplification targets sorted based on GC% of their gn and their respective primers according to the Primer3 design parameters and another .txt file with optimal beacons for the selected probe sequence sorted according to increasing ΔG.

Sample input an output using Sars-Cov-2 sequence (Accession number: MT481992.1)
.txt file containing selected probe sequence and best molecular beacons generated
.txt file containing target sequences found and best primer set generated for each

The PDF below contains documentation and analysis of the various iterations of molecular beacon designs produced from the output of the combined code.

Wet Lab Protocols

The wet lab notebook we used when validating the biological parts designed to identify a characteristic sequence of SARS-CoV-2 for the proposed molecular diagnostic assay is linked below. All protocols, methodology, and results are present. We thank Benchling for letting us use their platform for free as students!

Wet Lab Notebook

Hardware


Microfluidic Device Design 1

After determining the biologics of the chip extensive research was done to develop a microfluidic device for the biologics to occur in. The following design was the first iteration of our Microfluidic Device:

After consulting with various experiments we decided to change up the chip size, channel dimensions, channel pattern, increase the number of outlets, and develop space in our chip design for a control reaction along with many other modifications.

Microfluidic Device Design 2

The following design is the 2nd iteration of our Microfluidic Device:

Device Design 3 - Radial

The following design is the 3rd iteration of our device. We decided to try a radial design due to the difficulty associated with aliquoting the correct amount of saliva, manually actuating flow at appropriate times without costly pumps, and starting reactions. The image below shows the three cross sections of this radial device, each corresponding to the initiation of a separate reaction as the device is turned by the user, allowing the sample and reagents to travel through the device. An cross-sectional view showing the suggested arrangement of reaction chambers is also included.

Microfluidic Circuit Heater Engineering Design

Alongside the development of our microfluidic chip, our team set out to engineer a portable heating system for the rapid heating and cooling of our microfluidic chip. The biologics of our device are temperature and time-dependent. Thus, using feed-back control systems and electrical circuit designs an economical and efficient portable heating circuit was designed and tested. See the PDF below for full documentation of the circuit design iterations, analysis, and results.


References

  1. S. (2020, February 24). What is intercultural competence? Retrieved October 24, 2020, from https://www.monash.edu/arts/monash-intercultural-lab/about-the-monash-intercultural-lab/what-is-intercultural-competence
  2. Monroe, W. T., & Haselton, F. R. (2003). Molecular Beacon Sequence Design Algorithm. BioTechniques, 34(1), 68-73. doi:10.2144/03341st02
  3. GeneLink. (2020). TaqMan - Molecular Beacons - Fluorescent Molecular Probes. Retrieved from http://www.genelink.com/newsite/products/amp&analysis.asp
  4. Biosynthesis. (n.d.). Design rules for Molecular Beacons. Retrieved from https://www.biosyn.com/tew/Design-rules-for-Molecular-Beacons.aspx
  5. Tsourkas, A. (2003). Hybridization kinetics and thermodynamics of molecular beacons. Nucleic Acids Research, 31(4), 1319-1330. doi:10.1093/nar/gkg212
  6. Higgins, M., Ravenhall, M., Ward, D., Phelan, J., Ibrahim, A., Forrest, M. S., . . . Campino, S. (2018). PrimedRPA: Primer design for recombinase polymerase amplification assays. Bioinformatics, 35(4), 682-684. doi:10.1093/bioinformatics/bty701
  7. New England Biolabs. (2019). GUIDElines for optimization of Tth Argonaute (TtAgo) reactions: NEB. Retrieved from https://international.neb.com/tools-and-resources/usage-guidelines/guidelines-for-optimization-of-tth-argonaute-ttago-reactions