Antimicrobial resistance and tolerance pose escalating global health threats, necessitating reproducible and accessible tools for antimicrobial susceptibility testing (AST). While disk diffusion assays (DDAs) and Epsilometer tests (Etests) are widely used, there are limited open-source tools to analyze them. We present J-AST, a free, open-source, web-based platform for analyzing both DDAs and Etests. It provides automated and interactive annotation of regions of interest and metadata management, and quantifies microbial resistance and tolerance. J-AST outputs correlate strongly with those of existing tools, and equivalent DDA and Etest results correlate strongly with each other. The automated MIC detection achieved >90% agreement with manual readouts. J-AST is deployable both as desktop software and cloud service, unifies automated analysis with interactive review, and advances both fundamental research and clinical AST workflows.

The ubiquitous RNA chaperone Hfq is involved in the regulation of key biological pro-cesses in many species across the bacterial kingdom. In the opportunistic human path-ogen Klebsiella pneumoniae, deletion of the hfq gene affects the global transcriptome, virulence, and stress resistance; however, the ligands of the major RNA- binding protein in this species have remained elusive. In this study, we have combined transcriptomic, co- immunoprecipitation, and global RNA interactome analyses to compile an inventory of conserved and species- specific RNAs bound by Hfq and to monitor Hfq- mediated RNA-RNA interactions. In addition to dozens of RNA-RNA pairs, our study revealed an Hfq- dependent small regulatory RNA (sRNA), DinR, which is processed from the 3 ' terminal portion of dinI mRNA. Transcription of dinI is controlled by the master regulator of the SOS response, LexA. As DinR accumulates in K. pneumoniae in response to DNA damage, the sRNA represses translation of the ftsZ transcript by occupation of the ribosome binding site. Ectopic overexpression of DinR causes depletion of ftsZ mRNA and inhibition of cell division, while deletion of dinR antagonizes cell elongation in the presence of DNA damage. Collectively, our work highlights the important role of RNA- based gene regulation in K. pneumoniae and uncovers the central role of DinR in LexA- controlled division inhibition during the SOS response

Abstract The continuous development of new microscopy techniques requires the parallel evolution of image analysis workflows. ImageJ provides a high level of accessibility to bioimage processing, which is still impeded by the necessity of developing scripts to achieve reproducibility, and to comply to the FAIR principles. We provide a visual language termed JIPipe that allows the construction of an ImageJ workflow purely by designing a flowchart. We already included over 1000 functions from ImageJ and its plugins. In return, ImageJ is extended with custom-designed algorithms, thus forming a symbiotic relationship with JIPipe. Our software includes a fully reproduceable and standardized project format, zero-cost scalability of pipelines, as well as automated data saving into an open format. JIPipe was already utilized to solve numerous demanding image analysis tasks, showcasing its wide applicability and adaptability. JIPipe contributes towards making bioimage analysis more accessible, thereby fostering collaborations between experimentalists and computer scientists.

Cryogels represent a class of porous sponge-like materials possessing unique properties including high-fidelity reproduction of tissue structure and maximized permeability. Their architecture is mainly based on an interconnected network of macropores that provides sufficient stability while allowing the movement of substances through the material. In most cryogel applications, the pore size is very important, especially when the material is used as a 3D scaffold for tissue culture, applied as a filter, or utilized as a membrane. In this study, poly(dimethylacrylamide-co-2-hydroxyethyl methacrylate) cryogels have been prepared by two preparation methods to investigate the reproducibility of homogeneous pore structures and pore sizes. Automated image analysis algorithms were developed to rapidly evaluate cryogel pore sizes based on scanning electron microscopy (SEM) images. The quantification approach contained a unique combination of classical and deep learning-based algorithms. To validate the accuracy of the two models, we compared the results obtained from automated SEM image analysis with those from manual pore size determinations and mercury intrusion porosimetry (MIP) measurements. Effect sizes were calculated to compare the results from manual and automated pore size measurements for the cryogel reproducibility series. 81% of the values obtained revealed only trivial differences, which strongly suggests that automated image analysis can reliably substitute the manual evaluation of cryogel pore sizes. The use of an adapted reactor setup yielded cryogels with heterogeneous morphologies in the absence of recognizable pore structures. With the conventional cryogel preparation using plastic syringes, the obtained cryogels represented highly reproducible morphologies and pore sizes in the range between 17 and 22 μm. Calculated effect sizes within the cryogel replicate series revealed only trivial differences between the obtained pore sizes in 83.5% or 99.4% of the data (classical approach and deep learning-based approach, respectively).

The soil community is a complex system characterized by predator-prey interactions. Fungi have developed effective strategies to defend themselves against predators.

Although multispectral optoacoustic tomography (MSOT) significantly evolved over the last several years, there is a lack of quantitative methods for analysing this type of image data. Current analytical methods characterise the MSOT signal in manually defined regions of interest outlining selected tissue areas. These methods demand expert knowledge of the sample anatomy, are time consuming, highly subjective and prone to user bias. Here we present our fully automated open-source MSOT cluster analysis toolkit Mcat that was designed to overcome these shortcomings. It employs a deep learning-based approach for initial image segmentation followed by unsupervised machine learning to identify regions of similar signal kinetics. It provides an objective and automated approach to quantify the pharmacokinetics and extract the biodistribution of biomarkers from MSOT data. We exemplify our generally applicable analysis method by quantifying liver function in a preclinical sepsis model whilst highlighting the advantages of our new approach compared to the severe limitations of existing analysis procedures.

Fungi of the genus Mortierella occur ubiquitously in soils where they play pivotal roles in carbon cycling, xenobiont degradation, and promoting plant growth. These important fungi are, however, threatened by micropredators such as fungivorous nematodes, and yet little is known about their protective tactics. We report that Mortierella verticillata NRRL 6337 harbors a bacterial endosymbiont that efficiently shields its host from nematode attacks with anthelmintic metabolites. Microscopic investigation and 16S ribosomal DNA analysis revealed that a previously overlooked bacterial symbiont belonging to the genus Mycoavidus dwells in M. verticillata hyphae. Metabolic profiling of the wild-type fungus and a symbiont-free strain obtained by antibiotic treatment as well as genome analyses revealed that highly cytotoxic macrolactones (CJ-12,950 and CJ13,357, syn. necroxime C and D), initially thought to be metabolites of the soil-inhabiting fungus, are actually biosynthesized by the endosymbiont. According to comparative genomics, the symbiont belongs to a new species (Candidatus Mycoavidus necroximicus) with 12% of its 2.2 Mb genome dedicated to natural product biosynthesis, including the modular polyketide-nonribosomal peptide synthetase for necroxime assembly. Using Caenorhabditis elegans and the fungivorous nematode Aphelenchus avenae as test strains, we show that necroximes exert highly potent anthelmintic activities. Effective host protection was demonstrated in cocultures of nematodes with symbiotic and chemically complemented aposymbiotic fungal strains. Image analysis and mathematical quantification of nematode movement enabled evaluation of the potency. Our work describes a relevant role for endofungal bacteria in protecting fungi against mycophagous nematodes.

Modern imaging techniques, such as lightsheet fluorescence microscopy (LSFM), allow the capture of whole organs in three spatial dimensions. The analysis of these big volume image data requires a combination of user-friendly and highly efficient tools. We here present MISA++, an image analysis framework that allows easy integration of custom high-performance C++ tools into third-party applications via standardized components for parallelization, data and parameter handling, command line interface, and communication with third-party applications. We demonstrate its capabilities by implementing a plugin for ImageJ that provides a graphical user interface for any application built with our framework, and a high-performance re-implementation of our Python-based algorithm to segment glomeruli in LSFM images of whole murine kidneys.

Motivation: The protein-coding sequences of messenger RNAs are the linear template for translation of the gene sequence into protein. Nevertheless, the RNA can also form secondary structures by intramolecular base-pairing. Results: We show that the nucleotide distribution within codons is biased in all taxa of life on a global scale. Thereby, RNA secondary structures that require base-pairing between the position 1 of a codon with the position 1 of an opposing codon (here named RNA secondary structure class c(1)) are under-represented. We conclude that this bias may result from the co-evolution of codon sequence and mRNA secondary structure, suggesting that RNA secondary structures are generally important in protein-coding regions of mRNAs. The above result also implies that codon position 2 has a smaller influence on the amino acid choice than codon position 1.

ABSTRACT The initial characterization and clustering of biological samples is a critical step in the analysis of any transcriptomic study. In many studies, principal component analysis (PCA) is the clustering algorithm of choice to predict the relationship of samples or cells based solely on differential gene expression. In addition to the pure quality evaluation of the data, a PCA can also provide initial insights into the biological background of an experiment and help researchers to interpret the data and design the subsequent computational steps accordingly. However, to avoid misleading clusterings and interpretations, an appropriate selection of the underlying gene sets to build the PCA and the choice of the most fitting principal components for the visualization are crucial parts. Here, we present PCAGO, an easy-to-use and interactive web service to analyze gene quantification data derived from RNA sequencing (RNA-Seq) experiments with PCA. The tool includes features such as read-count normalization, filtering of read counts by gene annotation, and various visualization options. Additionally, PCAGO helps to select appropriate parameters such as the number of genes and principal components to create meaningful visualizations. Availability and implementation The web service is implemented in R and freely available at pcago.bioinf.uni-jena.de . Contact martin.hoelzer@uni-jena.de