Difference between revisions of "Team:Newcastle/Results"

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           <p>Glyphosate is a herbicide that works by blocking the activity of the enzyme enolpyruvylshikimate-3-phosphate synthase (EPSPS), which converts carbohydrates derived from glycolysis and the pentose phosphate pathway to plant metabolites and aromatic amino acids.
 
           <p>Glyphosate is a herbicide that works by blocking the activity of the enzyme enolpyruvylshikimate-3-phosphate synthase (EPSPS), which converts carbohydrates derived from glycolysis and the pentose phosphate pathway to plant metabolites and aromatic amino acids.
 
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           We attempted to design a system capable of glyphosate detection. With little information regarding mechanisms of glyphosate interactions within the cell, we could not identify a simple system in which a responsive transcription factor was able to affect the production of a reporter gene. This is a common issue in many biosensor projects.
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           We attempted to design a system capable of glyphosate detection. With little information regarding mechanisms of glyphosate interactions within the cell, we could not identify a simple system in which a responsive transcription factor was able to affect the production of a reporter gene. This is a common issue in many biosensor projects.  
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To show the adaptor in action we chose to develop a part that would measure the level of glyphosate through the production of formaldehyde. There are known sensors for formaldehyde such as Tokyo’s 2012 biosensor (https://2012.igem.org/Team:TMU-Tokyo). Our design relies on the natural biochemical systems, the c-p lyase pathways, in <i>E. coli</i> to convert glyphosate to sarcosine. We then designed a part, SOX, based on the production of the enzyme sarcosine oxidase, encoded by soxA to convert sarcosine to formaldehyde ready for detection by a formaldehyde producing input module.
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           The mining of transcriptome data has previously been used to find responsive DNA elements to a molecule of interest (Groningen 2012). Therefore, we analysed differences in transcriptome data between glyphosate sensitive and insensitive plants. A number of genes were found which were differently expressed. However, it was determined that it is more likely that this differential expression was not due to glyphosate directly, but rather the aromatic amino acid starvation caused by EPSPS inhibition by glyphosate, making these systems unsuitable for direct glyphosate detection. Various other systems we designed were also far from ideal, with high levels of complexity and reliance on native plant machinery.
 
           The mining of transcriptome data has previously been used to find responsive DNA elements to a molecule of interest (Groningen 2012). Therefore, we analysed differences in transcriptome data between glyphosate sensitive and insensitive plants. A number of genes were found which were differently expressed. However, it was determined that it is more likely that this differential expression was not due to glyphosate directly, but rather the aromatic amino acid starvation caused by EPSPS inhibition by glyphosate, making these systems unsuitable for direct glyphosate detection. Various other systems we designed were also far from ideal, with high levels of complexity and reliance on native plant machinery.

Revision as of 20:09, 1 November 2017

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Our Experimental Results

Key Achievements

A condensed list of our most notable results


  • - Designed a novel framework for biosensor development
  • - Proved that multicellular biosensors are able to co-ordinate responses to input molecules through a proof-of-concept IPTG responsive biosensor
  • - Successful characterisation of a transpose-based “stand-by switch” capable of producing eforRed in the “OFF” state, and C4 AHL in the “ON” state
  • - Used a Design of Experiments approach to successfully optimise a cell-free system
  • - Improved the BLANK plasmid for promoter screening
  • - Expressed and characterised Sarcosine Oxidase, showing successful degradation of sarcosine to formaldehyde
  • - Designed, and began to construct, a variety of framework compatible systems, including a synthetic promoter library
  • - Determined optimal cell ratios from our multicellular model

Below is a diagram of our Sensynova Framework. Clicking on each part of the framework (e.g. detector modules) links to the relevant results.

Alternatively, at the bottom of this page are tabs which will show you results for every part of the project



Framework

Framework Chassis

Biochemical Adaptor

Target

Detector Modules

Multicellular Framework Testing

C12 HSL: Connector 1

Processor Modules

Framework in Cell Free Protein Synthesis Systems

C4 HSL: Connector 2

Reporter Modules



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