Periodic Reporting for period 2 - GLYCOSENSE (Polymer brush sensing arrays for the identification of pathogens)
Okres sprawozdawczy: 2019-02-01 do 2020-01-31
This action aims to generate chemical sensing systems which would allow for the rapid, cost-effective identification of disease-causing organisms, addressing an urgent clinical need. We are developing develop polymeric sensing arrays which will be used to identify pathogens in a range of complex environments, such as contaminated water supplies, and in clinical samples. Our polymers contain chemical functionalities that mimic the cellular surface, and enable the adhesion of pathogens using the same recognition mechanisms used by pathogens to infect cells. Interaction of analytes with these sensors produces a unique ‘fluorescence fingerprint,’ which can be used to identify the analyte. Fluorescence measurements are incredibly sensitive, enabling the detection of very small amounts of the analyte.
The ability to rapidly identify disease-causing organisms using cheap, portable devices would provide a huge boost to public health efforts, enabling the prevention of disease by identifying bacterial contamination. Such systems would also improve the management of existing illness, by quickly identifying the pathogen causing disease, would allowing doctors to choose optimal treatment options.
The ability to rapidly identify disease-causing organisms using cheap, portable devices would provide a huge boost to public health efforts, enabling the prevention of disease by identifying bacterial contamination. Such systems would also improve the management of existing illness, by quickly identifying the pathogen causing disease, would allowing doctors to choose optimal treatment options.
We have generated a library of synthetic polymers which mimic the carbohydrate functionalities displayed on the mammalian cellular surface, and explored their interactions with a number of carbohydrate-binding proteins (lectins) which display similar recognition behaviour to lectins found on bacterial surfaces. In order to detect interactions between the polymers and lectins, the polymers must be decorated with a suitable fluorophore, which changes in its ability to absorb and emit light in the presence of the lectins. We have screened a number of commercially-available and custom-synthesised fluorophores, and identified a suitable environmentally sensitive species which alters its behaviour upon exposure to the analytes. These optimised sensors have been screened against a model library of lectins comprised of plant- and bacterially- derived proteins, and multi-dimensional statistical analysis techniques have been used to identify the ‘fingerprint response’ of the array to each analyte. We have demonstrated that our sensing arrays are capable of discriminating these lectins from one another. We have explored the detection of analytes across a range of concentrations, and within complex mixtures, establishing proof-of-concept for our methodology. These results were used to guide the next phase of our study, which explored the identification of bacteria using techniques established.
Having established our detection methodology and established proof-of-concept for our system, our ongoing work focusses on the application of our sensing arrays to identify bacteria, including the identification of particularly virulent or antibiotic- resistant bacterial strains. We anticipate that we can develop sensing arrays capable of identifying these bacteria in complex media, providing the underpinning technology for new diagnostic devices.