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Seiffarth, J.

Publications and source records attributed to Seiffarth, J..

4 recordsLinked to original sources

DNA-intercalating antiphage molecules trigger abortive infection through mutual destruction and synergize with bacterial immunity

Bacteria deploy diverse antiphage defense systems, including small bioactive molecules providing protection at the multicellular level. DNA-intercalating anthracyclines, such as daunorubicin, exhibit broad antiphage activity, but the underlying mechanism has remained elusive. Here, we systematically screened the Escherichia coli BASEL phage collection to elucidate the mode of action of DNA-intercalating antiphage molecules. We identified taxonomically distinct clusters of susceptible viral groups and show that, in T5-like phages (Markadamsvirinae), daunorubicin blocks infection after first-step transfer (FST). In the presence of daunorubicin, continued expression of pre-early genes leads to abortive infection via mutual destruction, where both phage and host succumb. Analogous abortive-infection phenotypes occur across taxonomically diverse phages exposed to chemically distinct DNA-intercalating molecules. Notably, we show that daunorubicin synergizes with downstream nucleic acid-targeting defenses underscoring context-dependent outcomes. Together, these findings reveal how chemical defense contributes to the multilayered antiviral immunity and highlight the intricate interplay between mechanistic inhibition and infection outcome.

microbiology↗

ObiWan-Microbi: OMERO-based integrated workflow for annotating microbes in the cloud

SummaryReliable deep learning segmentation for microfluidic live-cell imaging requires comprehensive ground truth data. ObiWan-Microbi is a microservice platform combining the strength of state-of-the-art technologies into a unique integrated workflow for data management and efficient ground truth generation for instance segmentation, empowering collaborative semi-automated image annotation in the cloud. Availability and ImplementationObiWan-Microbi is open-source and available under the MIT license at https://github.com/hip-satomi/ObiWan-Microbi, along documentation and usage examples. Contactk.noeh@fz-juelich.de Supplementary informationSupplementary data are available online.

bioinformatics↗

microbeSEG: Accurate Cell Segmentation with OMERO Data Management

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

bioinformatics↗

CellSium - Versatile Cell Simulator for Microcolony Ground Truth Generation

SummaryTo train deep learning based segmentation models, large ground truth data sets are needed. To address this need in microfluidic live-cell imaging, we present CellSium, a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. We illustrate that the simulated images are suitable for training neural networks. Synthetic time-lapse videos with and without fluorescence, using programmable cell growth models, and simulation-ready 3D colony geometries for computational fluid dynamics (CFD) are also supported. Availability and ImplementationCellSium is free and open source software under the BSD license, implemented in Python, available at https://github.com/modsim/cellsium (DOI: 10.5281/zenodo.6193033), along with documentation, usage examples and Docker images. Contactk.noeh@fz-juelich.de Supplementary informationSupplementary data are available online.

bioinformatics↗