Publications by authors named "P Huss"
Article Synopsis
- * The study introduces Sensor-seq, a high-throughput platform that allows for the design and identification of aTF biosensors that can bind to a variety of non-native ligands, testing 17,737 variants of aTF TtgR against multiple compounds.
- * Sensor-seq successfully finds biosensors with high performance for various ligands and showcases their practical use through cell-free detection systems for specific drugs, enhancing the ability to create new biosensors beyond natural limitations.
Bacterial biotherapeutic delivery vehicles have the potential to treat a variety of diseases. This approach obviates the need to purify the recombinant effector molecule, allows delivery of therapeutics via oral or intranasal administration, and protects the effector molecule during gastrointestinal transit. Lactic acid bacteria have been broadly developed as therapeutic delivery vehicles though risks associated with the colonization of a genetically modified microorganism have so-far not been addressed.
View Article and Find Full Text PDFBacterial host factors regulate the infection cycle of bacteriophages. Except for some well-studied host factors (e.g.
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- Allosteric transcription factors (aTFs) are important tools for detecting various molecules but designing them to work with new compounds is difficult due to potential disruptions in their allosteric properties.
- The researchers developed a high-throughput platform called Sensor-seq that screened over 17,000 variants of the aTF TtgR to find biosensors that can bind to a range of non-native and native ligands.
- The study resulted in the identification of new biosensors with excellent specificity and effectiveness, and practical applications were demonstrated through cell-free detection systems for naltrexone and quinine.
Bacteriophages can adapt to new hosts by altering sequence motifs through recombination or convergent evolution. Where such motifs exist and what fitness advantage they confer remains largely unknown. We report a new method, Metagenomic Sequence Informed Functional Scoring (Meta-SIFT), to discover sequence motifs in metagenomic datasets that can be used to engineer phage activity.
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