Biology subjects

Mazur, J.

Publications and source records attributed to Mazur, J..

6 recordsLinked to original sources

bioRxiv2026

View Tomo: Context-aware targeting and analysis in electron cryo-tomography

Electron cryo-tomography (cryoET) resolves cellular structure in three dimensions, yet region selection is still typically based on two-dimensional projection images. Here, we introduce View Tomo, a workflow for rapid acquisition of low-magnification tomograms that enables screening, targeting and analysis in 3D. View Tomo tilt series are acquired in minutes at low dose (~3 e-/[A]2), producing high-contrast tomograms that remain compatible with subsequent high-resolution structural determination. We implemented View Tomo using an automated acquisition and reconstruction pipeline for rapid alignment. Across multiple viral and cellular systems, view tomograms revealed membrane remodelling events, assembly intermediates and cellular organisation that are difficult to identify in projection images. These data enabled targeted high-resolution imaging and quantitative analysis of spatial relationships within cells. View Tomo therefore extends cryoET workflows by improving target selection, enabling analysis of mesoscale organisation, and facilitating integration with correlative imaging approaches.

cell biology↗

bioRxiv2026

Can you run from your worries? The effects of exercise on anxiety-like behaviour and immune signaling in female and male mice

Exercise is a positive health behaviour associated with improved mood. However, the mechanisms underlying the benefits of exercise on affective health are unclear, particularly with respect to type of exercise and sex. Chronic exercise decreases neuroinflammation, which is linked to improvements in mood and anxiety. However, exercise is also a physiological stressor that can transiently upregulate systemic inflammation, and its effects on neuroinflammation are not well understood. This study examined how acute and chronic exercise affect circulating and brain cytokine levels and anxiety-related behaviour in young healthy male and female mice. In Experiment 1, mice were placed on a treadmill for a two-hour bout of moderate exercise. Two hours after exercise, animals were either tested in the open field or euthanized for measurement of cytokines (IL-1{beta}, TNF, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p70, IFN-{gamma}, KC/GRO). In Experiment 2, mice underwent an 8-week moderate treadmill exercise paradigm followed by open field testing and tissue collection. Acute exercise decreased time spent in the centre of the open field in males only, suggesting increased anxiety-like behaviour in males. Acute exercise increased IL-6 and decreased TNF in serum, and increased amygdala principal component 1 (loading IL-12p70, IL-10, IFN-{gamma}, and TNF) in both sexes. Chronic exercise increased open field centre entries, increased IL-6 in the prefrontal cortex, decreased TNF in the dorsal hippocampus, and had minimal effects on circulating cytokines in both sexes. These results demonstrate that the effects of exercise on anxiety-related behaviour and cytokine levels depend on recurrence, tissue, and brain region. NEW & NOTEWORTHYOur work highlights the contrast between anxiogenic and anxiolytic effects of acute versus chronic exercise, respectively, in healthy mice. Acute and chronic exercise differentially affected circulating and brain cytokines, providing insight into physiological adaptations to exercise. Both sexes demonstrated similar cytokine responses to exercise. These similarities are novel with respect to exercise research and noteworthy given sex differences in anxiety with respect to acute exercise.

neuroscience↗

bioRxiv2023

An engineered A549 cell line expressing CD13 and TMPRSS2 is permissive to clinical isolate of human coronavirus 229E

The lack of suitable in vitro culture model has hampered research on wild-type (WT) human coronaviruses. While 3D tissue or organ cultures have been instrumental for this purpose, such models are challenging, time-consuming, expensive and require extensive cell culture adaptation and directed evolution. Consequently, high-throughput applications are beyond reach in most cases. Here we developed a robust A549 cell line permissive to a human coronavirus 229E (HCoV-229E) clinical isolate by transducing CD13 and transmembrane serine protease 2 (TMPRSS2), henceforth referred to as A549++ cells. This modification allowed for productive infection, and a more detailed analysis showed that the virus might use the TMPRSS2-dependent pathway but can still bypass this pathway using cathepsin-mediated endocytosis. Overall, our data showed that A549++ cells are permissive to HCoV-229E clinical isolate, and applicable for further studies on HCoV-229E infectiology. Moreover, this line constitutes a uniform platform for studies on multiple members of the Coronaviridae family. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/545505v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@f954b9org.highwire.dtl.DTLVardef@1294d92org.highwire.dtl.DTLVardef@a3adddorg.highwire.dtl.DTLVardef@1eb8ea6_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2023

Colonization of axenic beet by rhizosphere bacteria takes place in discreet phases regardless of bioinculation with next generation bioinoculant

Bioinoculation can increase crop yields under environmental stress. Plant colonization by microbes is an example of succession, with its distinct phases differing in community structure and diversity. This process needs to be studied to determine the optimal timing for bioinoculation and its effects. Haere, we show that, regardless of bio-inoculation, soil type and plant genotype, bacteria colonize the rhizosphere of axenic beets and tissues in two phases, differing in bacterial load, nestedness, community structure, diversity and assembly mechanism, and associated with taproot development. Communities remained stable after five weeks of growth in soil. The alpha diversity was greater and the bacterial load was lower in the late samples than in the early ones. Time, soil type and genotype determined community structure but not alpha diversity, bacterial load, nestedness or assembly mechanisms both in the rhizosphere and in the endosphere. Inoculation changed the community structure and members of Pseudomonadota and Bacillota of low abundance in the inoculant were recruited by beets. Axenic beet colonization occurs through phases similar to other instances of microbial succession, and bacteria are recruited mostly randomly. The transition from the early to late phase involves a decrease in the bacterial load in plant tissues, which may be linked to plant growth and the arrest of bacterial cell division. Therefore, early inoculation seems to be favourable. Five weeks of growth in soil enabled formation of stable bacterial communities in both the rhizosphere and the endosphere. The influence of inoculation seems to be indirect, probably due to microbe-microbe interactions.

microbiology↗

bioRxiv2021

CONET: Copy number event tree model of evolutionary tumor history for single-cell data

Copy number alterations constitute important phenomena in tumor evolution. Whole genome single cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient MCMC procedure to search the space of possible model structures and parameters and utilizes both per-bin and per-breakpoint data. We introduce a range of model priors and penalties for efficient regularization. CONET achieves excellent performance on simulated data and for 260 cells from xenograft breast cancer sample.

bioinformatics↗

bioRxiv2019

Feature selection strategies for drug sensitivity prediction

Drug sensitivity prediction constitutes one of the main challenges in personalized medicine. The major difficulty of this problem stems from the fact that the sensitivity of cancer cells to treatment depends on an unknown subset of a large number of biological features. Although feature selection is the key to interpretable results and identification of potential biomarkers, a comprehensive assessment of feature selection methods for drug sensitivity prediction has so far not been performed. We propose feature selection approaches driven by prior knowledge of drug targets, target pathways, and gene expression signatures. We asses these methodologies on Genomics of Drug Sensitivity in Cancer (GDSC) dataset, a panel of around 1000 cell lines screened against multiple anticancer compounds. We compare our results with a baseline model utilizing genome-wide gene expression features and common data-driven feature selection techniques. Together, 2484 unique models were evaluated, providing a comprehensive study of feature selection strategies for the drug response prediction problem. For 23 drugs, the models achieve better predictive performance when the features are selected according to prior knowledge of drug targets and pathways. The best correlation of observed and predicted response using the test set is achieved for Linifanib (r=0.75). Extending the drug-dependent features with gene expression signatures yields models that are most predictive of drug response for 60 drugs, with the best performing example of Dabrafenib. Examples of how pre-selection of features benefits the model interpretability are given for Dabrafenib, Linifanib and Quizartinib. Based on GDSC drug data, we find that feature selection driven by prior knowledge tends to yield better results for drugs targeting specific genes and pathways, while models with the genome-wide features perform better for drugs affecting general mechanisms such as metabolism and DNA replication. For a significant group of the compounds, even a very small number of features based on simple drug properties is often highly predictive of drug sensitivity, can explain the mechanism of drug action and be used as guidelines for their prescription. In general, choosing appropriate feature selection strategies has the potential to develop interpretable models that are indicative for therapy design.

bioinformatics↗