Background

While some bacterial infections are seen as common place, others remain elusive to cure. Tuberculosis has currently infected a third of the world’s population, and 1.7 million people died from it in 2009. As shown, it is a worldwide problem that is prevalent in third world countries.

Tuberculosis exists in two stages. In its latent stage, the immune system has it under control and prevents the bacteria from reproducing. When it switches into its active stage as a person's immune system becomes compromised, it begins to multiply and the person starts showing symptoms of the disease. The genetic mechanisms that trigger the change between the two forms are not very well understood. The genetic network of tuberculosis is complicated and studying the interactions between genes can be time consuming, requiring the construction of many plasmids to study only one small part of the network. To look for new, faster ways of studying this problem we turned to synthetic biology. Instead of creating many vectors to study the interactions of genes, we aimed to create a plasmid with a designed circuit that would control the transcription of several tuberculosis genes using invertases. We chose to use non-pathogenic mycobacterium tuberculosis genes and transcription factors within non- pathogenic E.Coli for safety reasons.

Invertases are recombinases that will bind to recognition sites. Once bound, the DNA between two sites will invert and flip horizontally. This effectively acts as an on/off switch for transcription of that region of DNA as it is not available for DNA polymerase to bind to. In terms of circuit design, it will act as a not gate. Using them within a plasmid with several tuberculosis genes would allow us to check for regulatory measures such as negative feedback, etc.

An example of a construct built around the invertase architecture.

In addition to the novel cellular architecture, we also used a variety of software tools created by our computational teams (Boston University, Wellesley):

• G-nome Surfer Pro
  G-nome Surfer Pro is an integrated environment that allows for the viewing of prokaryotic genomic data and literature associated with the genome. As it is built on a Microsoft Surface, it encourages collaboration. We used the Primer Genie to design the primers for the tuberculosis genes and transcription factors.
• Trumpet
  Trumpet consists of a library of genes and promoters, which can be configured into any desired permutation or combination by treating the DNA with recombinases, which allows us to rewire the genes and switches and study all their combinations. It was used by the wetlab to help us map out where the invertases should go in our plasmids in order to correctly turn on and off the segments of DNA we want to study.
• PuppetShow
  This suite includes a high level programming language for specifying biological protocols commonly used in the laboratory, which are then executed by a liquid-handling robot with minimal user intervention. We used this in conjunction with the robot (described below) to create samples that were simultaneously created manually to compare the results.
• Clotho
  Clotho is used to mange, create and store new biological building blocks in community based repositories. It includes a suite of tools that include PuppetShow and Trumpet, which were designed specifically for this project. Other tools that were used were:
  • Bull Trowel
  • Spreadit Parts
  • Spreadit Vectors
  • Spreadit Features
  These were used to store a variety of parts, features, and allowed us to assemble vectors we were interested in creating electronically.
  • Optimus Primer-primer designer
  • Feature Chomp-reads in APE and GENBANK files and takes the features found within the file to a feature database
  • Batterboard-allowed us to electronically represent physical samples in the lab

Another way we looked into facilitating progress in studying genetic networks is through the use of automation:

• Robot
• QIA Cube

Through the usage of novel cell architecture, software, and hardware we show that studying complex genetic networks can be done faster than has been done in the past. It reduces the amount of work needed to be done, as well as the demand on materials and time.

Results

Being associated with two computational teams, the wetlab team was unique as our results not only consisted of creating novel forms of DNA, but evaluating the usefulness of the software tools our computational team produced. Our success was not only measured in the number of colonies we could get to grow, but also in how well we could incorporate these tools and give informative feedback.

Clotho: Key to Creation

Approach

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