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Biology subjects

Herndon, L. K.

Publications and source records attributed to Herndon, L. K..

2 recordsLinked to original sources

Glycan analysis probes inspired by human lectins for investigating host-microbe crosstalk

Human lectins are critical carbohydrate-binding proteins that recognize diverse glycoconjugates from microorganisms and can play a key role in host-microbe interactions. Despite their importance in immune recognition and pathogen binding, the specific glycan ligands and functions of many human lectins remain poorly understood. Using previous proof-of-concept studies on selected lectins as the foundation for this work, we present ten additional glycan analysis probes (GAPs) from a diverse set of human soluble lectins, offering robust tools to investigate glycan-mediated interactions. We describe a protein engineering platform that enables scalable production of GAPs that maintain native-like conformations and oligomerization states, equipped with functional reporter tags for targeted glycan profiling. We demonstrate that the soluble GAP reagents can be used in various applications, including glycan array analysis, mucin- binding assays, tissue staining, and microbe binding in complex populations. These capabilities make GAPs valuable for dissecting interactions relevant to understanding host responses to microbes. The tools can be used to distinguish microbial from mammalian glycans, which is crucial for understanding the cross-target interactions of lectins in a physiological environment where both glycan types exist. GAPs have potential as diagnostic and prognostic tools for detecting glycan alterations in chronic diseases, microbial dysbiosis, and immune-related conditions.

biochemistry↗

Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning

Wastewater-based epidemiology (WBE) is a powerful tool for monitoring community disease occurrence, but current methods for bacterial detection suffer from limited scalability, the need for a priori knowledge of the target organism, and the high degree of genetic similarity between different strains of the same species. Here, we show that surface-enhanced Raman spectroscopy (SERS) can be a scalable, label-free method for detection of bacteria in wastewater. We preferentially enhance Raman signal from bacteria in wastewater using positively-charged plasmonic gold nanorods (AuNRs) that electrostatically bind to the bacterial surface. Transmission cryoelectron microscopy (cryoEM) confirms that AuNRs bind selectively to bacteria in this wastewater matrix. We spike the bacterial species Staphylococcus epidermidis, Staphylococcus aureus, Serratia marcescens, and Escerichia coli and AuNRs into filter-sterilized wastewater, varying the AuNR concentration to achieve maximum signal across all pathogens. We then collect 540 spectra from each species, and train a machine learning (ML) model to identify bacterial species in wastewater. For bacterial concentrations of 109 cells/mL, we achieve an accuracy exceeding 85%. We also demonstrate that this system is effective at environmentally-realistic bacterial concentrations, with a limit of bacterial detection of 104 cells/mL. These results are a key first step toward a label-free, high-throughput platform for bacterial WBE.

bioengineering↗