Biology subjects

Galagan, J. E.

Publications and source records attributed to Galagan, J. E..

4 recordsLinked to original sources

bioRxiv2026

A nicotine biosensor derived from microbial screening

Physiologically relevant biosensors are in increasingly high demand, yet existing ones are severely limited in the number and type of biomarkers that are detected. The lack of biorecognition elements for most medically relevant biomarkers restricts the development of next generation single and continuous use monitors. Over billions of years, microbes have evolved a vast array of proteins to sense and metabolize small molecules, including those pertinent to human health. Of particular interest to us is the identification and subsequent integration of new microbial redox enzymes into electronic biosensors building off the established electrochemical technology of the continuous glucose monitor. Here we deploy genomic screening to identify analyte specific redox enzymes for biosensor development. As a proof of concept, we report the first electrochemical enzyme-based nicotine biosensor from a novel microbial enzyme, and use a variant with improved catalytic performance to enhance sensor performance. The biosensor detects nicotine over 0.4-100 M, a range relevant to nicotine concentrations present in active smoker sweat, saliva, gastric juice, and urine. This microbial mining approach for discovering redox enzymes expands the sensing parts toolbox available over conventional antibodies and aptamers.

bioengineering↗

bioRxiv2025

Wearable sweat nicotine biosensor based on a nicotineoxidoreductase and its natural electron acceptor

The advent of wearable biosensors is empowering clinical and lifestyle decision making. Nicotine is a drug of significant negative impact on public health given the prevalence of smoking and vaping. Yet noninvasive, continuous monitoring of nicotine is challenging due to lack of specific and sensitive biosensing elements. Here, we report a wearable highly sensitive nicotine biosensor and demonstrate on-body deployment of the sensor in a first-in-human study. The biosensor comprises nicotine oxidoreductase (NicA2) with its natural cytochrome c electron acceptor (CycN) as mediator: both proteins play a key role in the nicotine-degrading bacterium, Pseudomonas putida S16. The biosensor detects nicotine over four-orders of magnitude (0.1-100 M) with nanomolar sensitivity (LOD [~]33.6 nM), performs in the physiological pH range (pH 6-9), and is highly selective against common interferants and the major human nicotine metabolite, cotinine. Incorporation of the biosensor in a custom wearable device enables real-time measurement of nicotine from locally induced sweat on volunteer subjects and demonstrates higher analytical accuracy than gold-standard mass spectrometry.

bioengineering↗

bioRxiv2024

A Cryptic Prophage Transcription Factor Drives Phenotypic Changes via Host Gene Regulation

Cryptic prophages (CPs) are elements of bacterial genomes acquired from bacteriophage that infect the host cell and ultimately become stably integrated within the host genome. While some proteins encoded by CPs can modulate host phenotypes, the potential for Transcription Factors (TFs) encoded by CPs to impact host physiology by regulating host genes has not been thoroughly investigated. In this work, we report hundreds of host genes regulated by DicC, a DNA-binding TF encoded in the Qin prophage of Esherichia coli. We identified host-encoded regulatory targets of DicC that could be linked to known phenotypes of its induction. We also demonstrate that a DicC-induced growth defect is largely independent of other Qin prophage genes. Our data suggest a greater role for cryptic prophage TFs in controlling bacterial host gene expression than previously appreciated.

microbiology↗

bioRxiv2024

Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

bioinformatics↗