26 Sep 2023

Isolation and Characterization of Bacterial Isolates from Alaskan Permafrost for Synthetic Biology Applications

Abstract: Operations in the Artic and other cold regions require technologies that can perform reliably under extreme cold conditions. Permafrost and frozen soils harbor a wide range of microorganisms that have adapted to extremely low temperatures and have unique metabolic capabilities relevant to military operations and that could be exploited to develop biotechnologies optimized for cold environments. Cold-tolerant bacteria (psychrophiles and psychrotrophs) are critical to the development of synthetic biology technologies meant to work in cold environments like the Arctic. Using bacteria isolated from Alaskan permafrost, we applied an experimental pipeline to test the best candidates for use as biological platforms, or chassis, for low-temperature synthetic biology. Since synthetic biology constructs will perform only as well as their chassis, it is critical that circuits expected to perform under extreme cold conditions are housed in chassis that are adapted to those conditions. We identified one permafrost isolate, PTI8, related to Rhodococcus fascians, that is capable of growing from −1°C to at least 25°C and which we experimentally confirmed to uptake and express the broad host range plasmid pBTK519, suggesting PTI8 is a candidate for use as a novel cold-adapted chassis for synthetic biology.

8 Nov 2022

A 𝘬-Means Analysis of the Voltage Response of a Soil-Based Microbial Fuel Cell to an Injected Military-Relevant Compound (Urea)

Abstract: Biotechnology offers new ways to use biological processes as environmental sensors. For example, in soil microbial fuel cells (MFCs), soil electro-genic microorganisms are recruited to electrodes embedded in soil and produce electricity (measured by voltage) through the breakdown of substrate. Because the voltage produced by the electrogenic microbes is a function of their environment, we hypothesize that the voltage may change in a characteristic manner given environmental disturbances, such as the contamination by exogenous material, in a way that can be modelled and serve as a diagnostic. In this study, we aimed to statistically analyze voltage from soil MFCs injected with urea as a proxy for gross contamination. Specifically, we used 𝘬-means clustering to discern between voltage output before and after the injection of urea. Our results showed that the 𝘬-means algorithm recognized 4–6 distinctive voltage regions, defining unique periods of the MFC voltage that clearly identify pre- and postinjection and other phases of the MFC lifecycle. This demonstrates that 𝘬-means can identify voltage patterns temporally, which could be further improve the sensing capabilities of MFCs by identifying specific regions of dissimilarity in voltage, indicating changes in the environment.