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Title: Development of a genetic memory platform for detection of metals in water : use of mRNA and protein destabilization elements as a means to control autoinduction from the CUP1 promoter of Saccharomyces cerevisiae
Authors: Luther, Jamie L.
Goodson, Holly V.
Arnett, Clint M.
Keywords: Water--Pollution
Drinking water--Contamination
Heavy metals
Water quality
Water--Sensory evaluation
Publisher: Construction Engineering Research Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: ERDC/CERL ; TR-18-6
Abstract: As of 2017, the United Nations estimated that globally, over 80% of all wastewater is released into the environment without treatment. Often, the water is contaminated with toxic heavy metals, which ultimately enter potable water supplies. Contamination is especially prevalent in low- and middle-income countries, a fact that poses a significant threat to indigenous populations as well as to deployed troops in these areas. Standard methods for detecting trace levels of metals in water requires expensive equipment and highly trained personnel, both of which most developing countries lack. To address this issue, authors designed a fluorescent yeast biosensor capable of detecting a model heavy metal, copper. The biosensor was responsive to copper at concentrations from 1 to 100 ppm. However in the absence of copper, autoinduction was observed. To decrease autoinduction, researchers explored the use of various mRNA and protein destabilization motifs such as adenylate-uridylate-rich elements and peptide sequences rich in proline, glutamic acid, serine, and threonine. Prelimi-nary results demonstrated that the addition of destabilizing mRNA had no effect on levels of autoinduction within the system. However, integration of protein destabilizing amino acids effectively reduced autoinduction, but not without also decreasing the dynamic range of the sensor system.
Description: Technical Report
Gov't Doc #: ERDC/CERL TR-18-6
Rights: Approved for Public Release; Distribution is Unlimited
Size: 47 pages/3.312 Mb
Appears in Collections:Technical Report

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