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Biology subjects

Wilson, L. R.

Publications and source records attributed to Wilson, L. R..

3 recordsLinked to original sources

Disturbance-Diversity Relationships of Microbial Communities Change Based on Growth Substrate

Disturbance events can impact ecological community dynamics. Understanding how communities respond to disturbances, and how those responses can vary, is a challenge in microbial ecology. In this study, we grew a previously enriched specialized microbial community on either cellulose or glucose as a sole carbon source, and subjected them to one of five different disturbance regimes of varying frequencies ranging from low to high. Using 16S rRNA gene amplicon sequencing, we show that community structure is largely driven by substrate, but disturbance frequency affects community composition and successional dynamics. When grown on cellulose, bacteria in the genera Cellvibrio, Lacunisphaera, and Asticaccacaulis are the most abundant microbes. However, Lacunisphaera is only abundant in the lower disturbance frequency treatments, while Asticaccaulis is more abundant in the highest disturbance frequency treatment. When grown on glucose, the most abundant microbes are two Pseudomonas sequence variants, and a Cohnella sequence variant that is only abundant in the highest disturbance frequency treatment. Communities grown on cellulose exhibited a greater range of diversity (0.67-1.99 Shannon diversity and 1.38-5.25 Inverse Simpson diversity) that peak at the intermediate disturbance frequency treatment, or 1 disturbance every 3 days. Communities grown on glucose, however, ranged from 0.49-1.43 Shannon diversity and 1.37-3.52 Inverse Simpson with peak diversity at the greatest disturbance frequency treatment. These results demonstrate that the dynamics of a microbial community can vary depending on substrate and the disturbance frequency, and may potentially explain the variety of diversity-disturbance relationships observed in microbial ecosystems. Abstract ImportanceA generalizable diversity-disturbance relationship (DDR) of microbial communities remains a contentious topic. Various microbial systems have different DDRs. Rather than finding support or refuting specific DDRs, we investigated the underlying factors that lead to different DDRs. In this study, we measured a cellulose-enriched microbial communitys response to a range of disturbance frequencies from high to low, across two different substrates: cellulose and glucose. We demonstrate that the community displays a unimodal DDR when grown on cellulose, and a monotonically increasing DDR when grown on glucose. Our findings suggest that the same community can display different DDRs. These results suggest that the range of DDRs we observe across different microbial systems may be due to the nutritional resources microbial communities can access and the interactions between bacteria and their environment.

microbiology↗

FiPhA: An Open-Source Platform for Fiber Photometry Analysis

SignificanceFiber photometry is a widely used technique in modern behavioral neuroscience, employing genetically encoded fluorescent sensors to monitor neural activity and neurotransmitter release in awake-behaving animals, However, analyzing photometry data can be both laborious and time-consuming. AimWe propose the FiPhA (Fiber Photometry Analysis) app, which is a general-purpose fiber photometry analysis application. The goal is to develop a pipeline suitable for a wide range of photometry approaches, including spectrally resolved, camera-based, and lock-in demodulation. ApproachFiPhA was developed using the R Shiny framework and offers interactive visualization, quality control, and batch processing functionalities in a user-friendly interface. ResultsThis application simplifies and streamlines the analysis process, thereby reducing labor and time requirements. It offers interactive visualizations, event-triggered average processing, powerful tools for filtering behavioral events and quality control features. ConclusionsFiPhA is a valuable tool for behavioral neuroscientists working with discrete, event-based fiber photometry data. It addresses the challenges associated with analyzing and investigating such data, offering a robust and user-friendly solution without the complexity of having to hand-design custom analysis pipelines. This application thus helps standardize an approach to fiber photometry analysis.

neuroscience↗

Partial or complete loss of norepinephrine differentially alters contextual fear and catecholamine release dynamics in hippocampal CA1

Contextual fear learning is heavily dependent on the hippocampus. Despite evidence that catecholamines contribute to contextual encoding and memory retrieval, the precise temporal dynamics of their release in the hippocampus during behavior is unknown. In addition, new animal models are required to probe the effects of altered catecholamine synthesis on release dynamics and contextual learning. Utilizing GRABNE and GRABDA sensors, in vivo fiber photometry, and two new mouse models of altered locus coeruleus norepinephrine (LC-NE) synthesis, we investigate norepinephrine (NE) and dopamine (DA) release dynamics in dorsal hippocampal CA1 during contextual fear conditioning. We report that aversive foot-shock increases both NE and DA release in dorsal CA1, while freezing behavior associated with recall of fear memory is accompanied by decreased release. Partial loss of LC-NE synthesis reveals that NE release dynamics are modulated by sex. Moreover, we find that recall of recent fear memory is sensitive to both partial and complete loss of LC-NE synthesis throughout prenatal and postnatal development, similar to prior observations of mice with global loss of NE synthesis beginning postnatally. In contrast, remote recall is compromised only by complete loss of LC-NE synthesis beginning prenatally. Overall, these findings provide novel insights into the role of NE in contextual fear and the precise temporal dynamics of both NE and DA during freezing behavior, and highlight a complex relationship between genotype, sex, and NE signaling.

neuroscience↗