Biology subjects

Evans, N. J.

Publications and source records attributed to Evans, N. J..

3 recordsLinked to original sources

bioRxiv2025

Activation of the Pseudomonas aeruginosa Glycerol Regulon Promotes Antibiotic Persistence and Modulates Virulence Phenotypes

Chronic infections with Pseudomonas aeruginosa are a major contributor of lung decline in persons with cystic fibrosis (pwCF). P. aeruginosa establishes life-long infections in the CF airway by utilizing various adaptation strategies to persist, including altering the expression of metabolic genes to acquire nutrients that are abundant in the CF airway. Glycerol, which is readily available in the airway, is imported and metabolized by genes in the glp regulon, which is under the control of the GlpR repressor. Previously, it has been shown that the loss of GlpR results in increased biofilm development in a CF-adapted isolate of P. aeruginosa compared to a wound isolate. Based on the increased biofilm phenotype previously observed and because biofilms are associated with increased antibiotic tolerance, we questioned whether GlpR plays a role in mediating antibiotic resistance of P. aeruginosa. In this report, we show that loss of GlpR increases tobramycin resistance of a CF-adapted isolate in synthetic sputum and in airway epithelial cell and Drosophila melanogaster colonization models. Further, transcriptomics analysis revealed that CF-adapted mutants of glpR overexpresses genes involved in multidrug tolerance and chronic infection phenotypes such as alginate. In summary, our study illustrates that activation of the glycerol (glp) regulon may promote P. aeruginosa persistence in the CF airway.

microbiology↗

bioRxiv2024

Graph Structured Neural Networks for Perturbation Biology

1Computational modeling of perturbation biology identifies relationships between molecular elements and cellular response, and an accurate understanding of these systems will support the full realization of precision medicine. Traditional deep learning, while often accurate in predicting response, is unlikely to capture the true sequence of involved molecular interactions. Our work is motivated by two assumptions: 1) Methods that encourage mechanistic prediction logic are likely to be more trustworthy, and 2) problem-specific algorithms are likely to outperform generic algorithms. We present an alternative to Graph Neural Networks (GNNs) termed Graph Structured Neural Networks (GSNN), which uses cell signaling knowledge, encoded as a graph data structure, to add inductive biases to deep learning. We apply our method to perturbation biology using the LINCS L1000 dataset and literature-curated molecular interactions. We demonstrate that GSNNs outperform baseline algorithms in several prediction tasks, including 1) perturbed expression, 2) cell viability of drug combinations, and 3) disease-specific drug prioritization. We also present a method called GSNNExplainer to explain GSNN predictions in a biologically interpretable form. This work has broad application in basic biological research and pre-clincal drug repurposing. Further refinement of these methods may produce trustworthy models of drug response suitable for use as clinical decision aids. Availability and implementationOur implementation of the GSNN method is available at https://github.com/nathanieljevans/GSNN. All data used in this work is publicly available.

bioinformatics↗

bioRxiv2022

Imputed genomes and haplotype-based analyses of the Picts of early medieval Scotland reveal fine-scale relatedness between Iron Age, early medieval and the modern people of the UK.

The origins and ancestry of the Picts of early medieval Scotland (ca. AD 300-900) has been traditionally seen as a problem, prompted in part by exotic medieval origin myths, their enigmatic symbols and inscriptions, and the meagre textual evidence. The Picts, first mentioned in the late 3rd century AD resisted the Romans and went on to form a powerful kingdom that ruled over a large territory in northern Britain. In the 9th and 10th centuries Gaelic language, culture and identity became dominant, transforming the Pictish realm into Alba, the precursor to the medieval kingdom of Scotland. To date, no comprehensive analysis of Pictish genomes has been published, and questions about their biological relationships to other cultural groups living in Britain remain unanswered. Here we present two high-quality Pictish genomes (2.4 and 16.5X coverage) from central and northern Scotland dated from the 5th-7th century which we impute and co-analyse with >8,300 previously published ancient and modern genomes. Using allele frequency and haplotype-based approaches, we can firmly place the Pictish genomes within the Iron Age gene pool in Britain and demonstrate local biological affinity. We also demonstrate the presence of population structure within Pictish groups, with Orcadian Picts being genetically distinct from their mainland contemporaries. When investigating Identity-By-Descent (IBD) with present-day genomes, we observe broad affinities between the mainland Pictish genomes and the present-day people living in western Scotland, Wales, Northern Ireland and Northumbria, but less with the rest of England, the Orkney islands and eastern Scotland - where the political centres of Pictland were located. The pre-Viking Age Orcadian Picts evidence a high degree of IBD sharing across modern Scotland, Wales, Northern Ireland, and the Orkney islands, demonstrating substantial genetic continuity in the Orkney for the last ~2,000 years. Analysis of mitochondrial DNA diversity at the Pictish cemetery of Lundin Links (n = 7) reveals absence of female endogamy, with implications for broader social organisation. Overall, our study provides novel insights into the genetic affinities and population structure of the Picts and direct relationships between ancient and present-day groups of the UK.

genomics↗