Biology subjects

Benson, D.

Publications and source records attributed to Benson, D..

5 recordsLinked to original sources

bioRxiv2025

Development of a Customizable Pacing Protocol to Induce Persistent Atrial Fibrillation in Swine

IntroductionPersistent atrial fibrillation (AFib) research is dependent on large animal models to understand pathogenesis and test new treatments and therapeutic techniques. While various methods have been established to induce persistent AFib in large animals, including electrical, surgical, pharmacological, and genetic approaches, each has distinct limitations. The most recent being the device industry no longer providing off-target rapid atrial pacing programs for use in animals. This challenge requires the development of a new accessible model of inducing atrial fibrillation in large animals. Methods UsedWe developed a wireless pacing system using a Raspberry Pi Pico W microcontroller (Pico) programmed for variable pacing frequencies. The device is powered by a subcutaneously implanted 9V battery. The Picos built-in Wi-Fi capabilities enable remote connection and real-time adjustment of pacing frequency output. This protocol describes a prospective study in which swine will undergo chronic right atrial pacing for 3-4 weeks to induce persistent AFib. We tested our device in a domestic swine. Vascular access was established through the left jugular vein and an active fixation pacing lead (Medtronic 5076) was implanted under fluoroscopic guidance in the right atrium. Proper lead positioning and pacing function were confirmed through electrocardiographic monitoring of both atrial and ventricular capture. Preliminary ResultsOscilloscope testing demonstrated frequency and voltage output concordant with the programmed frequencies while real-time adjustments were made through the Wi-Fi interface. A cardiac pacing wire was placed in the right ventricle then relocated to the right atrium and successful pacing with capture was verified using an electrocardiogram. ConclusionsThis protocol will provide a system capable of capturing atrial tissue with confirmed wireless power transfer capabilities that has minimal tissue heating and is physiologically safe. Combined with our literature review findings that electrical atrial pacing methods are most effective for inducing persistent AFib, our device provides researchers with the potential for an improved tool for creating large animal models of persistent AFib.

bioengineering↗

bioRxiv2025

EMMAi: fast enzyme-allocation constraints in GEMs for improved biomass prediction across carbon sources

Genome-scale metabolic models (GEMs) predict emergent phenotypes by modeling the metabolic networks encoded in genomes. While GEMs have significantly advanced systems biology, metabolic engineering, biomedicine, and environmental science, they require extensive time and resources for manual curation, which can limit their utility in rapidly evolving research landscapes. Recent findings suggest that manually curated reactions can sometimes reduce prediction accuracy, indicating that integrating additional biologically grounded constraints may better capture emergent phenotypes. One promising approach is the incorporation of enzyme allocation constraints, which has been shown to enhance the predictive accuracy in metabolic models. Enzymatically constrained GEMs (ecGEMs) rely on enzyme turnover rates (kcat) and protein molecular weights (MWs) to account for intracellular resource limitations by introducing an enzyme pool variable and assigning costs to reactions, thereby simulating enzymatic resource constraints. Tools such as GECKO, AutoPACMEN, and ECMpy provide computational pipelines for ecGEM generation. However, these pipelines are often limited by their reliance on experimentally measured kcat values or deep learning-predicted values, such as those generated by DLKcat, which face challenges in predicting kinetics for enzymes dissimilar to their training data. Additionally, these methods frequently require extensive manual curation of kcat values based on empirical data, a time-intensive process that hampers scalability and applicability to non-model organisms. To address these limitations, we introduce EMMAi (Enzyme-constrained Metabolic Models with AI), a pipeline that fully automates the incorporation of enzyme constraints into GEMs. Unlike existing pipelines, EMMAi exclusively utilizes kcat values predicted by UniKP, an AI framework with improved accuracy over DLKcat, particularly for enzymes not present in training datasets. UniKP achieves a 13% improvement in correlation for unseen enzymes, enabling EMMAi to deliver ecGEMs with enhanced prediction accuracy without manual curation requirements. We evaluated EMMAi by applying it to three GEMs: two manually curated models, iJO1366 (Escherichia coli str. K-12 substr. MG1655) and iMO1056 (Pseudomonas aeruginosa PAO1), and one draft GEM constructed and gap-filled using CarveMe. EMMAi-generated ecGEMs showed an average Pearson Correlation Coefficient (PCC) improvement of 0.27 for manually curated GEMs when compared to predicted and experimentally measured growth rates and Biolog readings. Notably, for the draft GEM of Pseudomonas aeruginosa PAO1, the PCC improved dramatically from -0.3 to 0.6. EMMAi demonstrates that automating the integration of enzyme allocation constraints using AI-predicted kinetic parameters significantly enhances the prediction accuracy of GEMs, even in the absence of manual curation. These results underscore EMMAis potential as a scalable, efficient, and accurate tool for advancing GEM-based research in systems biology, metabolic engineering, and beyond.

systems biology↗

bioRxiv2023

Longitudinal characterization of the captive adult and tadpole Wyoming toad (Anaxyrus baxteri) microbiome

At one time thought to be extinct in the wild, the Wyoming toad (Anaxyrus baxteri) is one of the most critically endangered North American amphibian species. Despite approximately 20 years of ex situ breeding and reintroduction programs, these animals remain functionally extinct in the wild. There is concern among those working in these programs that individuals bred in captivity fail to develop the proper microbiome to withstand the stressors of their native habitat following release. In related species, the skin microbiome has been shown to have a defensive function against common pathogens affecting these animals. However, the early-life microbiome of developing tadpoles in this species remains unknown and therefore this defensive function is unexplored in the Wyoming toad. This study employed 16S rRNA amplicon sequencing to document the baseline microbiome of tadpoles bred for release and captive adult breeder populations. To characterize microbiome development, multiple rounds of skin mucosal and cloacal swabs were obtained concurrently from adult Wyoming toads bred at Omahas Henry Doorly Zoo and Aquarium. Our results revealed significant differences between tadpole and adult microbiomes, as well as significant sex-dependent differences within the adult Wyoming toads, in terms of richness and composition. Thus, these findings have identified the baseline microbiome of this endangered species, and variables significantly influencing its composition. Ongoing studies of the only extant wild population are expected to identify taxa not present in captive toads, and potentially help design husbandry modifications to maximize survivability following reintroduction to the wild.

microbiology↗

bioRxiv2021

Inflammatory blockade prevents injury to the developing pulmonary gas exchange surface in preterm primates

Malformations of or injuries to the developing lung are associated with perinatal morbidity and mortality with lifelong consequences for subsequent pulmonary health. One fetal exposure linked with poor health outcomes is chorioamnionitis, which impacts up to 25-40% of preterm births. Severe chorioamnionitis with prematurity is associated with significantly increased risk of pulmonary disease and secondary infections in childhood, suggesting that fetal inflammation may significantly alter developmental ontogeny of the lung. To test this hypothesis, we used intra-amniotic lipopolysaccharide (LPS, endotoxin) to generate experimental chorioamnionitis in prenatal Rhesus macaque (Macaca mulatta), a model which shares critical structural and temporal aspects of human lung development. Inflammatory injury directly disrupts the developing gas exchange surface of the primate lung, with extensive damage to alveolar structure, particularly the close association and coordinated differentiation of alveolar type 1 pneumocytes and specialized alveolar capillary endothelium. Single cell RNA sequencing analysis defined a multicellular alveolar signaling niche driving alveologenesis which was extensively disrupted by perinatal inflammation, leading to loss of gas exchange surface and alveolar simplification similar to that found in chronic lung disease of newborns. Blockade of IL1{beta} and TNF ameliorated endotoxin-induced inflammatory lung injury by blunting stromal response to inflammation and modulating innate immune activation in myeloid cells, restoring structural integrity and key signaling networks in the developing alveolus. These data provide new insight into the pathophysiology of developmental lung injury and suggest that modulating inflammation is a promising therapeutic approach to prevent fetal consequences of chorioamnionitis.

developmental biology↗

bioRxiv2018

Profiling copy number alterations in cell-free tumour DNA using a single-reference

BackgroundThe accurate detection of copy number alterations from the analysis of circulating cell free tumour DNA (ctDNA) in blood is essential to realising the potential of liquid biopsies. However, currently available approaches require a large number of plasma samples from healthy individuals, sequenced using the same platform and protocols to act as a reference panel. Obtaining this reference panel can be challenging, prohibitively expensive and limits the ability to migrate to improved sequencing platforms and improved protocols.\n\nMethodsWe developed qCNV and sCNA-seq, two distinct tools that together provide a new approach for profiling somatic copy number alterations (sCNA) through the analysis of cell free DNA (cfDNA) without a reference panel. Our approach was designed to identify sCNA from cfDNA through the analysis of a single plasma sample and a matched normal DNA sample -both of which can be obtained from the same blood draw. qCNV is an efficient method for extracting read-depth from BAM files and sCNA-seq is a method that uses a probabilistic model of read depth to infer the copy number segmentation of the tumour. We compared the results from our pipeline to the established copy number profile of a cell-line, as well as the results from the plasma-Seq analysis of cfDNA-like mixtures and real, clinical data-sets.\n\nResultsWith a single, unmatched, germline reference sample, our pipeline recapitulated the known copy number profile of a cell-line and demonstrated similar results to those obtained from plasma-Seq. With less than 1X genome coverage, our approach identified clinically relevant sCNA in samples with as little as 20 % tumour DNA. When applied to plasma samples from cancer patients, our pipeline identified clinically significant mutations.\n\nConclusionsThese results show it is possible to identify therapeutically-relevant copy number mutations from plasma samples without the need to generate a reference panel from a large number of healthy individuals. Together with the range of sequencing platforms supported by our qCNV+sCNA-Seq pipeline, as well as the Galaxy implementation of this solution, this pipeline makes cfDNA profiling more accessible and makes it easier to identify sCNA from the plasma of cancer patients.

bioinformatics↗