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Title: Development and optimisation of an immunological and cellular toolkit for pollutant biosensing
Author: Strachan, G.
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 1999
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Anti-atrazine and anti-mecoprop single chain antibody fragments (scAbs) were cloned from monoclonal cell lines and expressed in Escherichia coli. The anti-atrazine and anti-mecoprop scAbs showed the same specificity for atrazine and mecoprop respectively as the parental mAb, but showed a 2-3 fold increase in sensitivity for free atrazine. Antibody fragments against mecoprop and diuron were stabilised by the introduction of inter domain disulphide bonds and were capable of remaining more than 90% functional in 10% methanol and other harsh denaturing conditions. BIAcore analysis and competition ELISAs confirmed the stabilised antibodies (stAbs) bound to free pesticide with the same affinity and specificity as the scAb fragments. Lux marked bacteria were used to predict the toxicity of 7 pesticides over a range of concentrations in aqueous solutions and finally in aqueous and non aqueous fractions extracted from soil. The toxicity of pesticide mixtures was further investigated and synergistic interactions identified. Lux marked E. coli HB101 cells and 4 anti-pesticide stAbs were used as a tool-kit to measure firstly toxicity and secondly to quantify the pesticides present in both the aqueous and non aqueous fractions of soil. The E. coli HB101 biosensor was able to detect pesticides from soil at 10-100 times lower concentrations than would have had an effect in water, showing improved sensitivity. The pesticide concentration solvent extracted soil was analysed using either stab fragment in an indirect competition ELISA or by HPLC and in each case the results were almost identical. This toolkit combines the specificity of stAb antibodies, as a rapid and robust alternative to chemical analysis, with toxicity assessment of lux biosensors as a safety net to identity presence of unknown toxins and toxin mixtures in the field.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available