Evolution-Inspired Design of Bacterial Biosensors

Bacterial signal transduction systems are critical tools for synthetic biology. However, while hundreds of thousands of systems have been identified in bacterial genomes, only a small subset have been characterized. In this work, we develop tools for scalable interrogation of bacterial signal transduction mechanisms at three key junctures: sensing, processing, and actuation. In the first portion of this work, we identify 13 novel antimicrobial peptide (AMP)-activators of the S. Typhimurium PhoPQ two component system (TCS). We characterize the activation profiles of a subset of these AMPs in S. Typhimurium and E. coli using a variety of PhoPQ responsive promoters. We show that PhoPQ homologs from extraintestinal pathogenic E. coli and K. pneumoniae, which occupy in vivo niches, exhibit distinct activation profiles, providing new insights into the specificities, mechanisms, and evolutionary dynamics of TCS-mediated peptide sensing in bacteria. In the second portion, we propose a novel workflow for the prediction of transcription factor (TF) operator sites based on a modified phylogenetic footprinting framework. We increase operator prediction accuracy from 45% to 83% when compared to leading methods and use this framework to predict operator sites for thousands of bacterial TFs. We then use these predictions to interrogate structural features that confer DNA-binding specificity to the TetR family of TFs. Finally, in the third portion of this work, we develop new methods for analyzing signal transduction in bacterial TCSs. We characterize interactions between membrane-bound sensor histidine kinases (SKs) and their cognate response regulators (RRs) using the protein language model ESM2. We show that the model is able to identify highly coevolving residues at the SK-RR interface. We then train a dedicated pairing model to predict TCS interactions from their amino acid sequences. We experimentally test our models with the E. coli SK BasS and the S. PCC6803 RR CcaR. Using model-guided mutagenesis, we successfully engineer signal transduction between BasS and CcaR in vivo. As a whole, this work provides a framework for characterizing signal input, processing, and response of bacterial signal transduction systems at scale.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Systems/Synthetic/Phys Biology

Grantor

Rice University

Year dc:date.issued

2025

Author dc:creator

• Hunt, Maxwell G.

Advisor dc:contributor.advisor

• Tabor, Jeffrey J

• synthetic biology
• two component systems
• transcription factor
• operator

Statement dc:rights

• Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1911/118616

OAI identifier oai:identifier

oai:repository.rice.edu:1911/118616