Biology subjects

Dean, P.

Publications and source records attributed to Dean, P..

3 recordsLinked to original sources

bioRxiv2025

Adaptive trade-offs between niche-driven defence system selection and horizontal gene transfer suggests clinical success in Acinetobacter spp.

2.IntroductionBacteria face relentless selective pressure from both antibiotics and bacteriophages, driving the evolution of diverse defence systems and shaping horizontal gene transfer (HGT) dynamics. Gap StatementThe relationship between bacterial defence system repertoires and horizontal gene transfer in clinically relevant Acinetobacter species remains poorly understood, limiting our ability to predict resistance evolution and design targeted phage therapies. AimTo characterize defence arsenal across Acinetobacter species and investigate their associations with different HGT markers MethodologyWe performed a comparative genomic analysis of 132 genomes from 18 Acinetobacter species, focusing on the interplay between defence architectures and HGT markers. ResultsOur result reveals pronounced differences in defence system repertoires between lineages: while most Acinetobacter spp. harbour a multilayered arsenal, the clinically dominant A. baumannii international clone 2 (IC2) exhibits a depleted but specialised defence landscape, notably enriched in the phosphorothioation-based SspBCDE system and nearly devoid of restriction-modification systems. Strikingly, many defence systems display mutual exclusivity, and defence system is tightly linked to the presence of mobile elements, antibiotics, and heavy metal resistance. Plasmid-borne defence systems, especially BREX, are prevalent, highlighting the role of mobile elements in distributing both immunity and resistance traits. These patterns suggest that successful clinical lineages finely balance immune defences and genetic plasticity, facilitating rapid adaptation under antimicrobial selection and phage threats. ConclusionThese findings provide insights into evolutionary trade-offs underpinning multidrug resistance and have implications for designing targeted phage therapies against recalcitrant Acinetobacter infections. 3. Data summaryAll bioinformatics analysis workflows, custom scripts, and R code are publicly available at https://github.com/vikos77/Acinetobacter-defence-systems. The repository includes bash scripts for genome downloading and tool execution (scripts 1-10), R scripts for statistical analysis and visualization (scripts 1-9), and conda environment files for reproducible software installation. Complete analysis protocols are documented in the repository README and individual script headers. All software versions and parameters are specified in the code documentation. 4. Impact StatementThis study reveals how defence system composition in Acinetobacter species is shaped by ecological niche and linked to horizontal gene transfer, antibiotic resistance, and mobile genetic elements. Notably, the clinically dominant A. baumannii IC2 lineage exhibits a streamlined defence repertoire favouring genetic plasticity, facilitating rapid adaptation under antibiotic and phage pressure. These findings enhance our understanding of resistance evolution and support the rational design of phage-based therapies targeting multidrug-resistant pathogens.

bioinformatics↗

bioRxiv2024

PAN deadenylase is required for mitosis in response to microtubule stress in yeast and humans

HIGHLIGHTSO_LIPAN2-PAN3 catalytic activity is required for mitotic robustness under MT stress C_LIO_LICatalytic-dead Pan2-D1020A phenocopies pan2{Delta}: spindle defects and G2/M arrest C_LIO_LILoss of Pan2 catalytic activity amplifies nocodazole-induced changes in CLB1 and CLN3 mRNA C_LIO_LIPAN2 depletion in human cells produces multipolar spindles under MT stress C_LI IN BRIEFVerma et al. show that PAN2-PAN3 deadenylase catalytic activity is selectively required for mitotic fidelity under microtubule stress. Yeast pan2{Delta} cells exhibit spindle defects, G2/M arrest, and altered CLB1 and CLN3 mRNA levels under nocodazole. PAN2 knockdown in human cells produces multipolar spindles, demonstrating evolutionary conservation. Cytoplasmic deadenylation by PAN2-PAN3 and CCR4-NOT regulates mRNA stability and translation, but the contribution of individual deadenylases to mitotic progression is poorly defined. Here we show that PAN2-PAN3 catalytic activity is selectively required for mitotic robustness under microtubule stress. In budding yeast, the catalytically inactive Pan2-D1020A mutant fails to complement pan2{Delta} nocodazole sensitivity, leading to G2/M arrest, spindle defects, and increased cell death. pan2{Delta} exhibits synthetic genetic interactions with tubulin and tubulin-folding genes, and RNA-seq reveals dysregulation of cell-cycle transcripts including the cyclins CLB1 and CLN3. The phenotype is stress-conditional: pan2{Delta} cells proliferate normally without drug. In mammalian cells, PAN2 depletion sensitizes HEK293A cells to nocodazole and colchicine and produces prolonged metaphase with multipolar spindles. Together, these findings define a conserved, stress-conditional role for PAN2-PAN3 deadenylase activity in safeguarding mitotic fidelity when microtubules are compromised. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/591760v3_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@19e6662org.highwire.dtl.DTLVardef@53ba18org.highwire.dtl.DTLVardef@de9c99org.highwire.dtl.DTLVardef@fb0f9d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO See accompanying graphical abstract file. C_FIG

cell biology↗

bioRxiv2019

A methodology for morphological feature extraction and unsupervised cell classification

Cell morphology is an important indicator of cell state, function, stage of development, and fate in both normal and pathological conditions. Cell shape is among key indicators used by pathologists to identify abnormalities or malignancies. With rapid advancements in the speed and amount of biological data acquisition, including images and movies of cells, computer-assisted identification and analysis of images becomes essential. Here, we report on techniques for recognition of cells in microscopic images and automated cell shape classification. We illustrate how our unsupervised machine-learning-based approach can be used to classify distinct cell shapes from a large number of microscopic images.\n\nTechnical AbstractWe develop a methodology to segment cells from microscopy images and compute quantitative descriptors that characterize their morphology. Using unsupervised techniques for dimensionality reduction and density-based clustering, we perform label-free cell shape classification. Cells are identified with minimal user input using mathematical morphology and region-growing segmentation methods. Physical quantities describing cell shape and size (including area, perimeter, Feret diameters, etc.) are computed along with other features including shape factors and Hus image moments.\n\nCorrelated features are combined to obtain a low-dimensional (2-D or 3-D) embedding of data points corresponding to individual segmented cell shapes. Finally, a hierarchical density-based clustering algorithm (HDBSCAN) is used to classify cells. We compare cell classification results obtained from different combinations of features to identify a feature set that delivers optimum classification performance for our test data consisting of phase-contrast microscopy images of a pancreatic-cancer cell line, MIA PaCa-2.

bioinformatics↗