Biology subjects

Wildenhain, J.

Publications and source records attributed to Wildenhain, J..

4 recordsLinked to original sources

bioRxiv2023

Estradiol propionate reduction of nuclear size in prostate cancer lines lacking DHRS7suggests DHRS7 absence as a biomarker for the effectiveness of estrogen therapy

Increased nuclear size correlates with lower survival rates for prostate cancer and is a hallmark of late-stage androgen-insensitive tumors. The short-chain dehydrogenase/reductase (SDR) family member DHRS7 was suggested as a marker for prostate cancer grading because it is lost in late-stage androgen-insensitive tumors. Here we find that loss of DHRS7 from the early-stage LNCaP prostate cancer cell line increases nuclear size, potentially explaining the nuclear size increase observed in higher-grade prostate tumors. Exogenous expression of DHRS7 in the late-stage PC3 prostate cancer cell line correspondingly decreases nuclear size. We separately tested 80 compounds from the Microsource Spectrum library for their ability to restore normal nuclear size to PC3 cells, finding estradiol propionate had the same effect as re-expression of DHRS7 in the PC3 cells. However, the drug had no effect on LNCaP cells or PC3 cells re-expressing DHRS7. We speculate that reported beneficial effects of estrogens in late-stage prostate cancer may target a pathway which is only active in cells lacking DHRS7 that have increased nuclear size and propose DHRS7 as a potential biomarker for the likely effectiveness of estrogen-based treatments.

cancer biology↗

bioRxiv2022

Nanoscaled discovery of a shunt rifamycin from Salinispora arenicola using a three-colour GFP-tagged Staphylococcus aureus macrophage infection assay.

Antimicrobial resistance has emerged as an urgent global public health threat, and development of novel therapeutics for treating infections caused by multi-drug resistant bacteria is urgent. Staphylococcus aureus is a major human and animal pathogen, responsible for high levels of morbidity and mortality worldwide. The intracellular survival of S. aureus in macrophages contributes to immune evasion, dissemination, and resilience to antibiotic treatment. Here, we present a confocal fluorescence imaging assay for monitoring macrophage infection by GFP-tagged Staphylococcus aureus as a front-line tool to identify antibiotic leads. The assay was employed in combination with nanoscaled chemical analyses to facilitate the discovery of a novel, active rifamycin analogue. Our findings indicate a promising new approach to the identification of anti-microbial compounds with macrophage intracellular activity. The novel antibiotic identified here may represent a useful addition to our armoury in tackling the silent pandemic of antimicrobial resistance.

cell biology↗

bioRxiv2020

Chemical-Genetic Interrogation of Nuclear Size Control Reveals Cancer-Specific Effects on Cell Migration and Invasion

Lower survival rates for many cancer types correlate with increases or decreases in nuclear size/scaling in a tumor-type/tissue-specific manner. Postulating that nuclear size changes confer a fitness advantage on tumor cells, we screened for FDA/EMA-approved compounds that reverse tumor nuclear size changes in cell lines from three such tumor types: prostate adenocarcinoma, colonic adenocarcinoma, and small-cell squamous lung cancer. We found distinct, largely non-overlapping sets of compounds that either rectify or exacerbate nuclear size changes for each tumor type. Nuclear size phenotypes across cell lines clustered particular classes of compounds including serotonin uptake inhibitors, cyclo-oxygenase inhibitors, beta-adrenergic receptor agonists, monoamine oxidase inhibitors, and Na+/K+ ATPase inhibitors. Nearly all compounds selected for further investigation inhibited cell migration and/or invasion, suggesting that targeting nuclear size control pathways in chemotherapy regimens could improve patient survival.

cancer biology↗

bioRxiv2019

Segmenting nuclei in brightfield images with neural networks

Identifying nuclei is a standard first step to analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not use fluorescence can also carry useful information. Here, we demonstrate that it is possible to accurately segment nuclei directly from brightfield images using deep learning. We confirmed that three convolutional neural network architectures can be adapted for this task, with U-Net achieving the best overall performance, Mask R-CNN providing an additional benefit of instance segmentation, and DeepCell proving too slow for practical application. We found that accurate segmentation is possible using as few as 16 training images and that models trained on images from similar cell lines can extrapolate well. Acquiring data from multiple focal planes further helps distinguish nuclei in the samples. Overall, our work liberates a fluorescence channel reserved for nuclear staining, thus providing more information from the specimen, and reducing reagents and time required for preparing imaging experiments.

bioinformatics↗