Heterologous expression of microbial nitroreductases for TNT degradation in transgenic animals
Perusse, D.; Tepper, K.; Sychla, A.; Beach, S.; Smanski, M.; Maselko, M.
Show abstract
TNT (2,4,6-trinitrotoluene) from unexploded ordinances is a common environmental pollutant near munitions factories, military training sites, and areas of armed conflict. As physical and chemical approaches for TNT remediation are costly and difficult to scale, in situ bioremediation is an attractive alternative. TNT is a phytoaccumulative pollutant. Herbivores engineered to detoxify TNT are a potentially cost-effective platform to bioremediate large areas of contaminated sites while grazing or browsing. As a first step towards engineering herbivores with metabolic capabilities for TNT degradation, we engineered the animal genetic model, Drosophila melanogaster, to screen a library of microbial nitroreductases that can catalyse the initial degradation steps required for TNT degradation. We find strong, cofactor-dependent activity in fly lysates engineered to express NsfA from Escherichia coli, and NsfI from Enterobacter cloacae.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Bioremediation of industrial pollutants by insects expressing a fungal laccase 97%
- Cre/lox-mediated chromosomal integration of biosynthetic gene clusters for heterologous expression in Aspergillus nidulans 94%
- Rational design of novel fluorescent enzyme biosensors for direct detection of strigolactones 93%
Similar papers in this journal
- Development of an eco-friendly RNAi yeast attractive targeted sugar bait that silences the Shaker gene in spotted-wing drosophila, Drosophila suzukii 94%
- Reduced processing and toxin binding associated with resistance to Vip3Aa in a resistant strain of fall armyworm (Spodoptera frugiperda) from Louisiana 93%
- Metabolic functional redundancy of the CYP9A subfamily members leads to P450-mediated lambda-cyhalothrin resistance in Cydia pomonella 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.