Biology subjects

Gunsch, C. K.

Publications and source records attributed to Gunsch, C. K..

3 recordsLinked to original sources

bioRxiv2026

Effect of alginate encapsulation on growth and viability of polycyclic aromatic hydrocarbon-degrading bacteria varies by environment, species, and capsule design

Polycyclic aromatic hydrocarbons (PAHs) are hazardous organic contaminants for which microbial bioaugmentation is a promising remediation strategy, but poor persistence of introduced microorganisms can limit efficacy. Encapsulation may improve persistence, yet the influence of capsule design, microbial species, and environmental conditions on performance remains poorly understood. We evaluated alginate encapsulation of the PAH-degrading bacteria Pseudomonas putida and Novosphingobium aromaticivorans across nutrient conditions and capsule formulations. Encapsulation effects varied by species and medium, influencing growth rate, maximum cell density, overall growth, and lag time; notably, encapsulation shortened lag time of N. aromaticivorans in sRB15 medium (36.9 h to 3.9-5.3 h). Enumeration methods also affected apparent cell recovery. After 8 weeks, encapsulation had no significant effect on P. putida but resulted in increased concentrations of N. aromaticivorans relative to planktonic cultures (1.22 x 10; vs. 2.05 x 10; CFU/mL). Capsule composition further influenced cell retention: increasing alginate approximately doubled capsule-associated cell concentrations, while chitosan coatings reduced cell concentrations within capsules without affecting external concentrations. These findings demonstrate that the benefits of encapsulation are species- and environment-dependent and that capsule formulation can be tuned to influence bacterial persistence and release, informing the design of encapsulated inoculants for bioaugmentation applications.

bioengineering↗

bioRxiv2025

A simple library preparation modification significantly reduces barcode crosstalk in ONT multiplexed sequencing

Barcode crosstalk is a potential source of error in indexed multiplex sequencing that can mimic cross-contamination and generate false-positive signals, particularly in experiments with low-biomass samples and imbalanced designs. Here we identify and quantify barcode crosstalk in multiplexed Oxford Nanopore sequencing and introduce post-ligation pooling (PLP). PLP is a drop-in library-preparation modification that prevents barcode crosstalk, rather than simply mitigating its effects. These results highlight the potential for barcode crosstalk to have detrimental effects in multiplexed long-read sequencing and establish PLP as an immediately adoptable mitigation, especially where negative controls and low-abundance signals inform biological interpretation.

genomics↗

bioRxiv2025

Engineering microbial consortia for distributed signal processing

A critical goal in biology is deducing input signals from measurable readouts. Genetic circuits have successfully been designed to respond to specific analytes and produce quantifiable outputs; however, multiplexed signal processing remains challenging. This limitation is partially due to crosstalk, or sensors non-specific responses to unintended signals. While strategies to achieve orthogonality are promising, they are time-intensive and context-dependent. Here, we introduce a new, generalizable approach that leverages microbial consortia to distribute sensory functions and computational methods to disentangle signals. Compartmentalizing sensor circuits within distinct populations simplifies experimental optimization by allowing individual populations to be exchanged without requiring genetic modifications. Our computational pipeline combines mechanistic modeling with machine learning to decode microbial communities unique temporal responses and predict multiple input concentrations. We demonstrated this platforms versatility in a variety of contexts: measuring signals with high crosstalk, detecting antibiotics with natural microbial communities, and quantifying chemicals in hospital sink wastewater. Our findings highlight how combining microbial engineering with computational strategies can produce robust, scalable biosensors for diverse applications.

synthetic biology↗