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The diagnosis of early-stage mycosis fungoides (MF) is challenging owing to shared clinical and histopathological features with benign inflammatory dermatoses. Recent evidence has shown that deep learning (DL) can assist pathologists in cancer classification, but this field is largely unexplored for cutaneous lymphomas. This study evaluates DL in distinguishing early-stage MF from benign inflammatory dermatoses using a unique dataset of 924 H&E-stained whole-slide images from skin biopsies, including 233 patients with early-stage MF and 353 patients with benign inflammatory dermatoses. All patients with MF were diagnosed after clinicopathological correlation. The classification accuracy of weakly supervised DL models was benchmarked against 3 expert pathologists. The highest performance on a temporal test set was at ×200 magnification (0.50 μm per pixel resolution), with a mean area under the curve of 0.827 ± 0.044 and a mean balanced accuracy of 76.2 ± 3.9%. This nearly matched the 77.7% mean balanced accuracy of the 3 expert pathologists. Most (63.5%) attention heatmaps corresponded well with the pathologists' region of interest. Considering the difficulty of the MF versus benign inflammatory dermatoses classification task, the results of this study show promise for future applications of weakly supervised DL in diagnosing early-stage MF. Achieving clinical-grade performance will require larger multi-institutional datasets and improved methodologies, such as multimodal DL with incorporation of clinical data., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Tuberculosis (TB) is a world health challenge the treatment of which is impacted by the rise of drug-resistant strains. Thus, there is an urgent need for new antitubercular compounds and novel approaches to improve current TB therapy. The zebrafish animal model has become increasingly relevant as an experimental system. It has proven particularly useful during early development for aiding TB drug discovery, supporting both the discovery of new insights into mycobacterial pathogenesis and the evaluation of therapeutical toxicity and efficacy in vivo. In this review, we summarize the past two decades of zebrafish-Mycobacterium marinum research and discuss its contribution to the field of bioactive antituberculosis therapy development., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Fungi is a highly diverse group of eukaryotic organisms that live under an extremely wide range of environmental conditions. Nowadays, there is a fundamental focus on observing how biodiversity varies on different spatial scales, in addition to understanding the environmental factors which drive fungal biodiversity. Metabarcoding is a high-throughput DNA sequencing technology that has positively contributed to observing fungal communities in environments. While the DNA sequencing data generated from metabarcoding studies are available in public archives, this valuable data resource is not directly usable for fungal biodiversity investigation. Additionally, due to its fragmented storage and distributed nature, it is not immediately accessible through a single user interface. We developed the MycoDiversity DataBase User Interface (https://mycodiversity.liacs.nl) to provide direct access and retrieval of fungal data that was previously inaccessible in the public domain. The user interface provides multiple graphical views of the data components used to reveal fungal biodiversity. These components include reliable geo-location terms, the reference taxonomic scientific names associated with fungal species and the standard features describing the environment where they occur. Direct observation of the public DNA sequencing data in association with fungi is accessible through SQL search queries created by interactively manipulating topological maps and dynamic hierarchical tree views. The search results are presented in configurable data table views that can be downloaded for further use. With the MycoDiversity DataBase User Interface, we make fungal biodiversity data accessible, assisting researchers and other stakeholders in using metabarcoding studies for assessing fungal biodiversity., Competing Interests: No conflict of interest to declare Disclaimer: This article is (co-)authored by any of the Editors-in-Chief, Managing Editors or their deputies in this journal., (Irene Martorelli, Aram Pooryousefi, Haike van Thiel, Floris J Sicking, Guus J Ramackers, Vincent Merckx, Fons J Verbeek.)

Automated nanoparticle phenotyping is a critical aspect of high-throughput drug research, which requires analyzing nanoparticle size, shape, and surface topography from microscopy images. To automate this process, we present an instance segmentation pipeline that partitions individual nanoparticles on microscopy images. Our pipeline makes two key contributions. Firstly, we synthesize diverse and approximately realistic nanoparticle images to improve robust learning. Secondly, we improve the BlendMask model to segment tiny, overlapping, or sparse particle images. Specifically, we propose a parameterized approach for generating novel pairs of single particles and their masks, encouraging greater diversity in the training data. To synthesize more realistic particle images, we explore three particle placement rules and an image selection criterion. The improved one-stage instance segmentation network extracts distinctive features of nanoparticles and their context at both local and global levels, which addresses the data challenges associated with tiny, overlapping, or sparse nanoparticles. Extensive experiments demonstrate the effectiveness of our pipeline for automating nanoparticle partitioning and phenotyping in drug research using microscopy images.

As an important technique for data pre-processing, outlier detection plays a crucial role in various real applications and has gained substantial attention, especially in medical fields. Despite the importance of outlier detection, many existing methods are vulnerable to the distribution of outliers and require prior knowledge, such as the outlier proportion. To address this problem to some extent, this article proposes an adaptive mini-minimum spanning tree-based outlier detection (MMOD) method, which utilizes a novel distance measure by scaling the Euclidean distance. For datasets containing different densities and taking on different shapes, our method can identify outliers without prior knowledge of outlier percentages. The results on both real-world medical data corpora and intuitive synthetic datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art methods., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Li, Wang, Verbeek, Schultz and Liu.)

In image captioning models, the main challenge in describing an image is identifying all the objects by precisely considering the relationships between the objects and producing various captions. Over the past few years, many methods have been proposed, from an attribute-to-attribute comparison approach to handling issues related to semantics and their relationships. Despite the improvements, the existing techniques suffer from inadequate positional and geometrical attributes concepts. The reason is that most of the abovementioned approaches depend on Convolutional Neural Networks (CNNs) for object detection. CNN is notorious for failing to detect equivariance and rotational invariance in objects. Moreover, the pooling layers in CNNs cause valuable information to be lost. Inspired by the recent successful approaches, this paper introduces a novel framework for extracting meaningful descriptions based on a parallelized capsule network that describes the content of images through a high level of understanding of the semantic contents of an image. The main contribution of this paper is proposing a new method that not only overrides the limitations of CNNs but also generates descriptions with a wide variety of words by using Wikipedia. In our framework, capsules focus on the generation of meaningful descriptions with more detailed spatial and geometrical attributes for a given set of images by considering the position of the entities as well as their relationships. Qualitative experiments on the benchmark dataset MS-COCO show that our framework outperforms state-of-the-art image captioning models when describing the semantic content of the images.

Motivation: The Flavivirus genus includes several important pathogens, such as Zika, dengue and yellow fever virus. Flavivirus RNA genomes contain a number of functionally important structures in their 3' untranslated regions (3'UTRs). Due to the diversity of sequences and topologies of these structures, their identification is often difficult. In contrast, predictions of such structures are important for understanding of flavivirus replication cycles and development of antiviral strategies., Results: We have developed an algorithm for structured pattern search in RNA sequences, including secondary structures, pseudoknots and triple base interactions. Using the data on known conserved flavivirus 3'UTR structures, we constructed structural descriptors which covered the diversity of patterns in these motifs. The descriptors and the search algorithm were used for the construction of a database of flavivirus 3'UTR structures. Validating this approach, we identified a number of domains matching a general pattern of exoribonuclease Xrn1-resistant RNAs in the growing group of insect-specific flaviviruses., Availability and Implementation: The Leiden Flavivirus RNA Structure Database is available at https://rna.liacs.nl. The search algorithm is available at https://github.com/LeidenRNA/SRHS., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Background: The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus., Results: Here we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis., Conclusions: Reaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.

Toll-like receptor (TLR) signaling via myeloid differentiation factor 88 protein (MyD88) has been indicated to be involved in the response to wounding. It remains unknown whether the putative role of MyD88 in wounding responses is due to a control of leukocyte cell migration. The aim of this study was to explore in vivo whether TLR2 and MyD88 are involved in modulating neutrophil and macrophage cell migration behavior upon zebrafish larval tail wounding. Live cell imaging of tail-wounded larvae was performed in tlr2 and myd88 mutants and their corresponding wild type siblings. In order to visualize cell migration following tissue damage, we constructed double transgenic lines with fluorescent markers for macrophages and neutrophils in all mutant and sibling zebrafish lines. Three days post fertilization (dpf), tail-wounded larvae were studied using confocal laser scanning microscopy (CLSM) to quantify the number of recruited cells at the wounding area. We found that in both tlr2 -/- and myd88 -/- groups the recruited neutrophil and macrophage numbers are decreased compared to their wild type sibling controls. Through analyses of neutrophil and macrophage migration patterns, we demonstrated that both tlr2 and myd88 control the migration direction of distant neutrophils upon wounding. Furthermore, in both the tlr2 and the myd88 mutants, macrophages migrated more slowly toward the wound edge. Taken together, our findings show that tlr2 and myd88 are involved in responses to tail wounding by regulating the behavior and speed of leukocyte migration in vivo ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hu, van Steijn, Li, Verbeek, Cao, Merks and Spaink.)

Background and Purpose: There is a clear need for innovation in anti-tuberculosis drug development. The zebrafish larva is an attractive disease model in tuberculosis research. To translate pharmacological findings to higher vertebrates, including humans, the internal exposure of drugs needs to be quantified and linked to observed response., Experimental Approach: In zebrafish studies, drugs are usually dissolved in the external water, posing a challenge to quantify internal exposure. We developed experimental methods to quantify internal exposure, including nanoscale blood sampling, and to quantify the bacterial burden, using automated fluorescence imaging analysis, with isoniazid as the test compound. We used pharmacokinetic-pharmacodynamic modelling to quantify the exposure-response relationship responsible for the antibiotic response. To translate isoniazid response to humans, quantitative exposure-response relationships in zebrafish were linked to simulated concentration-time profiles in humans, and two quantitative translational factors on sensitivity to isoniazid and stage of infection were included., Key Results: Blood concentration was only 20% of the external drug concentration. The bacterial burden increased exponentially, and an isoniazid dose corresponding to 15 mg·L -1 internal concentration (minimum inhibitory concentration) leads to bacteriostasis of the mycobacterial infection in the zebrafish. The concentration-effect relationship was quantified, and based on that relationship and the translational factors, the isoniazid response was translated to humans, which correlated well with observed data., Conclusions and Implications: This proof of concept study confirmed the potential of zebrafish larvae as tuberculosis disease models in translational pharmacology and contributes to innovative anti-tuberculosis drug development, which is very clearly needed., (© 2020 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Fungi have crucial roles in ecosystems, and are important associates for many organisms. They are adapted to a wide variety of habitats, however their global distribution and diversity remains poorly documented. The exponential growth of DNA barcode information retrieved from the environment is assisting considerably the traditional ways for unraveling fungal diversity and detection. The raw DNA data in association to environmental descriptors of metabarcoding studies are made available in public sequence read archives. While this is potentially a valuable source of information for the investigation of Fungi across diverse environmental conditions, the annotation used to describe environment is heterogenous. Moreover, a uniform processing pipeline still needs to be applied to the available raw DNA data. Hence, a comprehensive framework to analyses these data in a large context is still lacking. We introduce the MycoDiversity DataBase, a database which includes public fungal metabarcoding data of environmental samples for the study of biodiversity patterns of Fungi. The framework we propose will contribute to our understanding of fungal biodiversity and aims to become a valuable source for large-scale analyses of patterns in space and time, in addition to assisting evolutionary and ecological research on Fungi.

Background: Cardiotoxicity, characterized by severe cardiac dysfunction, is a major problem in patients treated with different classes of anticancer drugs. Development of predictable human-based models and assays for drug screening are crucial for preventing potential drug-induced adverse effects. Current animal in vivo models and cell lines are not always adequate to represent human biology. Alternatively, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) show great potential for disease modelling and drug-induced toxicity screenings. Fully automated high-throughput screening of drug toxicity on hiPSC-CMs by fluorescence image analysis is, however, very challenging, due to clustered cell growth patterns and strong intracellular and intercellular variation in the expression of fluorescent markers., Results: In this paper, we report on the development of a fully automated image analysis system for quantification of cardiotoxic phenotypes from hiPSC-CMs that are treated with various concentrations of anticancer drugs doxorubicin or crizotinib. This high-throughput system relies on single-cell segmentation by nuclear signal extraction, fuzzy C-mean clustering of cardiac α-actinin signal, and finally nuclear signal propagation. When compared to manual segmentation, it generates precision and recall scores of 0.81 and 0.93, respectively., Conclusions: Our results show that our fully automated image analysis system can reliably segment cardiomyocytes even with heterogeneous α-actinin signals.

Zebrafish is a useful modeling organism for the study of vertebrate development, immune response, and metabolism. Metabolic studies can be aided by mathematical reconstructions of the metabolic network of zebrafish. These list the substrates and products of all biochemical reactions that occur in the zebrafish. Mathematical techniques such as flux-balance analysis then make it possible to predict the possible metabolic flux distributions that optimize, for example, the turnover of food into biomass. The only available genome-scale reconstruction of zebrafish metabolism is ZebraGEM. In this study, we present ZebraGEM 2.0, an updated and validated version of ZebraGEM. ZebraGEM 2.0 is extended with gene-protein-reaction associations (GPRs) that are required to integrate genetic data with the metabolic model. To demonstrate the use of these GPRs, we performed an in silico genetic screening for knockouts of metabolic genes and validated the results against published in vivo genetic knockout and knockdown screenings. Among the single knockout simulations, we identified 74 essential genes, whose knockout stopped growth completely. Among these, 11 genes are known have an abnormal knockout or knockdown phenotype in vivo (partial), and 41 have human homologs associated with metabolic diseases. We also added the oxidative phosphorylation pathway, which was unavailable in the published version of ZebraGEM. The updated model performs better than the original model on a predetermined list of metabolic functions. We also determined a minimal feed composition. The oxidative phosphorylation pathways were validated by comparing with published experiments in which key components of the oxidative phosphorylation pathway were pharmacologically inhibited. To test the utility of ZebraGEM2.0 for obtaining new results, we integrated gene expression data from control and Mycobacterium marinum -infected zebrafish larvae. The resulting model predicts impeded growth and altered histidine metabolism in the infected larvae.

Accurate segmentation of zebrafish from bright-field microscope images is crucial to many applications in the life sciences. Early zebrafish stages are used, and in these stages the zebrafish is partially transparent. This transparency leads to edge ambiguity as is typically seen in the larval stages. Therefore, segmentation of zebrafish objects from images is a challenging task in computational bio-imaging. Popular computational methods fail to segment the relevant edges, which subsequently results in inaccurate measurements and evaluations. Here we present a hybrid method to accomplish accurate and efficient segmentation of zebrafish specimens from bright-field microscope images. We employ the mean shift algorithm to augment the colour representation in the images. This improves the discrimination of the specimen to the background and provides a segmentation candidate retaining the overall shape of the zebrafish. A distance-regularised level set function is initialised from this segmentation candidate and fed to an improved level set method, such that we can obtain another segmentation candidate which preserves the explicit contour of the object. The two candidates are fused using heuristics, and the hybrid result is refined to represent the contour of the zebrafish specimen. We have applied the proposed method on two typical datasets. From experiments, we conclude that the proposed hybrid method improves both efficiency and accuracy of the segmentation of the zebrafish specimen. The results are going to be used for high-throughput applications with zebrafish.

To improve the effectiveness and efficiency of optical projection tomography (OPT) 3-D reconstruction, we present a fast post-processing pipeline, including cropping, background subtraction, center of rotation (COR) correction, and 3-D reconstruction. Regarding to the COR correction, a novel algorithm based on interest point detection of sinogram is proposed by considering the principle of OPT imaging. Instead of locating the COR on single sinogram, we select equally spaced sinograms in the detected full range of specimen to make the located COR more convincing. The presented post-processing pipeline is implemented in a parallel manner and the experiments show that the average runtime for each image of size 1036 ×1360 ×400 pixels is less than 1 min. To quantify and compare the reconstructed results of different COR correction approaches, the coefficient of variation instead of variance is employed. The results indicate that the proposed COR correction outperforms the three traditional COR alignment approaches in terms of effectiveness and computational complexity.

High-throughput imaging is applied to provide observations for accurate statements on phenomena in biology and this has been successfully applied in the domain of cells, i.e. cytomics. In the domain of whole organisms, we need to take the hurdles to ensure that the imaging can be accomplished with a sufficient throughput and reproducibility. For vertebrate biology, zebrafish is a popular model system for High-throughput applications. The development of the Vertebrate Automated Screening Technology (VAST BioImager), a microscope mounted system, enables the application of zebrafish high-throughput screening. The VAST BioImager contains a capillary that holds a zebrafish for imaging. Through the rotation of the capillary, multiple axial-views of a specimen can be acquired. For the VAST BioImager, fluorescence and/or confocal microscopes are used. Quantitation of a specific signal as derived from a label in one fluorescent channel requires insight in the zebrafish volume to be able to normalize quantitation to volume units. However, from the setup of the VAST BioImager, a specimen volume cannot be straightforwardly derived. We present a high-throughput axial-view imaging architecture based on the VAST BioImager. We propose profile-based 3D reconstruction to produce 3D volumetric representations for zebrafish larvae using the axial-views. Volume and surface area can then be derived from the 3D reconstruction to obtain the shape characteristics in high-throughput measurements. In addition, we develop a calibration and a validation of our methodology. From our measurements we show that with a limited amount of views, accurate measurements of volume and surface area for zebrafish larvae can be obtained. We have applied the proposed method on a range of developmental stages in zebrafish and produced metrical references for the volume and surface area for each stage.

Cancer cells migrate from the primary tumour into surrounding tissue in order to form metastasis. Cell migration is a highly complex process, which requires continuous remodelling and re-organization of the cytoskeleton and cell-matrix adhesions. Here, we aimed to identify genes controlling aspects of tumour cell migration, including the dynamic organization of cell-matrix adhesions and cellular traction forces. In a siRNA screen targeting most cell adhesion-related genes we identified 200+ genes that regulate size and/or dynamics of cell-matrix adhesions in MCF7 breast cancer cells. In a subsequent secondary screen, the 64 most effective genes were evaluated for growth factor-induced cell migration and validated by tertiary RNAi pool deconvolution experiments. Four validated hits showed significantly enlarged adhesions accompanied by reduced cell migration upon siRNA-mediated knockdown. Furthermore, loss of PPP1R12B, HIPK3 or RAC2 caused cells to exert higher traction forces, as determined by traction force microscopy with elastomeric micropillar post arrays, and led to considerably reduced force turnover. Altogether, we identified genes that co-regulate cell-matrix adhesion dynamics and traction force turnover, thereby modulating overall motility behaviour.

Background: Endocytosis is regarded as a mechanism of attenuating the epidermal growth factor receptor (EGFR) signaling and of receptor degradation. There is increasing evidence becoming available showing that breast cancer progression is associated with a defect in EGFR endocytosis. In order to find related Ribonucleic acid (RNA) regulators in this process, high-throughput imaging with fluorescent markers is used to visualize the complex EGFR endocytosis process. Subsequently a dedicated automatic image and data analysis system is developed and applied to extract the phenotype measurement and distinguish different developmental episodes from a huge amount of images acquired through high-throughput imaging. For the image analysis, a phenotype measurement quantifies the important image information into distinct features or measurements. Therefore, the manner in which prominent measurements are chosen to represent the dynamics of the EGFR process becomes a crucial step for the identification of the phenotype. In the subsequent data analysis, classification is used to categorize each observation by making use of all prominent measurements obtained from image analysis. Therefore, a better construction for a classification strategy will support to raise the performance level in our image and data analysis system., Results: In this paper, we illustrate an integrated analysis method for EGFR signalling through image analysis of microscopy images. Sophisticated wavelet-based texture measurements are used to obtain a good description of the characteristic stages in the EGFR signalling. A hierarchical classification strategy is designed to improve the recognition of phenotypic episodes of EGFR during endocytosis. Different strategies for normalization, feature selection and classification are evaluated., Conclusions: The results of performance assessment clearly demonstrate that our hierarchical classification scheme combined with a selected set of features provides a notable improvement in the temporal analysis of EGFR endocytosis. Moreover, it is shown that the addition of the wavelet-based texture features contributes to this improvement. Our workflow can be applied to drug discovery to analyze defected EGFR endocytosis processes.

Both Petri nets and differential equations are important modeling tools for biological processes. In this paper we demonstrate how these two modeling techniques can be combined to describe biological gradient formation. Parameters derived from partial differential equation describing the process of gradient formation are incorporated in an abstract Petri net model. The quantitative aspects of the resulting model are validated through a case study of gradient formation in the fruit fly.

In biological research, Saccharomyces cerevisiae yeast cells are used to study the behaviour of proteins. This is a time consuming and not completely objective process. Hence, Image analysis platforms are developed to address these problems and to offer analysis per cell as well. The robust segmentation algorithms implemented in such platforms enables us to apply a machine learning approach on the measured cells. Such approach is based on a set of relevant individual cell features extracted from the microscope images of the yeast cells. In this paper, we composed a set of features to represent the intensity and morphology characteristics in a more sophisticated way. These features are based on first and second order histograms and wavelet-based texture measurement. To show the discrimination power of these features, we built a classification model to discriminate between different groups. The building process involved evaluation of a set of classification systems, data sampling techniques, data normalization schemes and attribute selection algorithms. The results show a significant ability to discriminate different cell strains and conditions; subsequently it reveals the benefits of the classification model based on the introduced features. This model is promising in revealing subtle patterns in future high-throughput yeast studies.

Objectives: To investigate how the genetic susceptibility gene DIO2 confers risk to osteoarthritis (OA) onset in humans and to explore whether counteracting the deleterious effect could contribute to novel therapeutic approaches., Methods: Epigenetically regulated expression of DIO2 was explored by assessing methylation of positional CpG-dinucleotides and the respective DIO2 expression in OA-affected and macroscopically preserved articular cartilage from end-stage OA patients. In a human in vitro chondrogenesis model, we measured the effects when thyroid signalling during culturing was either enhanced (excess T3 or lentiviral induced DIO2 overexpression) or decreased (iopanoic acid)., Results: OA-related changes in methylation at a specific CpG dinucleotide upstream of DIO2 caused significant upregulation of its expression (β=4.96; p=0.0016). This effect was enhanced and appeared driven specifically by DIO2 rs225014 risk allele carriers (β=5.58, p=0.0006). During in vitro chondrogenesis, DIO2 overexpression resulted in a significant reduced capacity of chondrocytes to deposit extracellular matrix (ECM) components, concurrent with significant induction of ECM degrading enzymes (ADAMTS5, MMP13) and markers of mineralisation (ALPL, COL1A1). Given their concurrent and significant upregulation of expression, this process is likely mediated via HIF-2α/RUNX2 signalling. In contrast, we showed that inhibiting deiodinases during in vitro chondrogenesis contributed to prolonged cartilage homeostasis as reflected by significant increased deposition of ECM components and attenuated upregulation of matrix degrading enzymes., Conclusions: Our findings show how genetic variation at DIO2 could confer risk to OA and raised the possibility that counteracting thyroid signalling may be a novel therapeutic approach., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Macrophage-expressed gene 1 (MPEG1) encodes an evolutionarily conserved protein with a predicted membrane attack complex/perforin domain associated with host defence against invading pathogens. In vertebrates, MPEG1/perforin-2 is an integral membrane protein of macrophages, suspected to be involved in the killing of intracellular bacteria by pore-forming activity. Zebrafish have 3 copies of MPEG1; 2 are expressed in macrophages, whereas the third could be a pseudogene. The mpeg1 and mpeg1.2 genes show differential regulation during infection of zebrafish embryos with the bacterial pathogens Mycobacterium marinum and Salmonella typhimurium. While mpeg1 is downregulated during infection with both pathogens, mpeg1.2 is infection inducible. Upregulation of mpeg1.2 is partially dependent on the presence of functional Mpeg1 and requires the Toll-like receptor adaptor molecule MyD88 and the transcription factor NFκB. Knockdown of mpeg1 alters the immune response to M. marinum infection and results in an increased bacterial burden. In Salmonella typhimurium infection, both mpeg1 and mpeg1.2 knockdown increase the bacterial burdens, but mpeg1 morphants show increased survival times. The combined results of these two in vivo infection models support the anti-bacterial function of the MPEG1/perforin-2 family and indicate that the intricate cross-regulation of the two mpeg1 copies aids the zebrafish host in combatting infection of various pathogens., (© 2014 S. Karger AG, Basel.)

In many situations, 3D cell cultures mimic the natural organization of tissues more closely than 2D cultures. Conventional methods for phenotyping such 3D cultures use either single or multiple simple parameters based on morphology and fluorescence staining intensity. However, due to their simplicity many details are not taken into account which limits system-level study of phenotype characteristics. Here, we have developed a new image analysis platform to automatically profile 3D cell phenotypes with 598 parameters including morphology, topology, and texture parameters such as wavelet and image moments. As proof of concept, we analyzed mouse breast cancer cells (4T1 cells) in a 384-well plate format following exposure to a diverse set of compounds at different concentrations. The result showed concentration dependent phenotypic trajectories for different biologically active compounds that could be used to classify compounds based on their biological target. To demonstrate the wider applicability of our method, we analyzed the phenotypes of a collection of 44 human breast cancer cell lines cultured in 3D and showed that our method correctly distinguished basal-A, basal-B, luminal and ERBB2+ cell lines in a supervised nearest neighbor classification method.

The function and structure of LysM-domain containing proteins are very diverse. Although some LysM domains are able to bind peptidoglycan or chitin type carbohydrates in bacteria, in fungi and in plants, the function(s) of vertebrate LysM domains and proteins remains largely unknown. In this study we have identified and annotated the six zebrafish genes of this family, which encode at least ten conceptual LysM-domain containing proteins. Two distinct sub-families called LysMD and OXR were identified and shown to be highly conserved across vertebrates. The detailed characterization of LysMD and OXR gene expression in zebrafish embryos showed that all the members of these sub-families are strongly expressed maternally and zygotically from the earliest stages of a vertebrate embryonic development. Moreover, the analysis of the spatio-temporal expression patterns, by whole mount and fluorescent in situ hybridizations, demonstrates pronounced LysMD and OXR gene expression in the zebrafish brain and nervous system during stages of larval development. None of the zebrafish LysMD or OXR genes was responsive to challenge with bacterial pathogens in embryo models of Salmonella and Mycobacterium infections. In addition, the expression patterns of the OXR genes were mapped in a zebrafish brain atlas., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Glucocorticoids (GCs) secreted after stress reduce adult hippocampal neurogenesis, a process that has been implicated in cognitive aspects of psychopathology, amongst others. Yet, the exact role of the GC receptor (GR), a key mediator of GC action, in regulating adult neurogenesis is largely unknown. Here, we show that GR knockdown, selectively in newborn cells of the hippocampal neurogenic niche, accelerates their neuronal differentiation and migration. Strikingly, GR knockdown induced ectopic positioning of a subset of the new granule cells, altered their dendritic complexity and increased their number of mature dendritic spines and mossy fiber boutons. Consistent with the increase in synaptic contacts, cells with GR knockdown exhibit increased basal excitability parallel to impaired contextual freezing during fear conditioning. Together, our data demonstrate a key role for the GR in newborn hippocampal cells in mediating their synaptic connectivity and structural as well as functional integration into mature hippocampal circuits involved in fear memory consolidation.

Focal adhesions (FAs) are specialized membrane-associated multi-protein complexes that link the cell to the extracellular matrix and enable cell proliferation, survival and motility. Despite the extensive description of the molecular composition of FAs, the complex regulation of FA dynamics is unclear. We have used photobleaching assays of whole cells to determine the protein dynamics in every single focal adhesion. We identified that the focal adhesion proteins FAK and paxillin exist in two different states: a diffuse cytoplasmic pool and a transiently immobile FA-bound fraction with variable residence times. Interestingly, the average residence time of both proteins increased with focal adhesion size. Moreover, increasing integrin clustering by modulating surface collagen density increased residence time of FAK but not paxillin. Finally, this approach was applied to measure FAK and paxillin dynamics using nocodazole treatment followed by washout. This revealed an opposite residence time of FAK and paxillin in maturing and disassembling FAs, which depends on the ventral and peripheral cellular position of the FAs.

In the study of complex patterns in biology, mathematical and computational models are emerging as important tools. In addition to experimental approaches, these modeling tools have recently been applied to address open questions regarding host-pathogen interaction dynamics, including the immune response to mycobacterial infection and tuberculous granuloma formation. We present an approach in which a computational model represents the interaction of the Mycobacterium infection with the innate immune system in zebrafish at a high level of abstraction. We use the Petri Net formalism to model the interaction between the key host elements involved in granuloma formation and infection dissemination. We define a qualitative model for the understanding and description of causal relations in this dynamic process. Complex processes involving cell-cell or cell-bacteria communication can be modeled at smaller scales and incorporated hierarchically into this main model; these are to be included in later elaborations. With the infection mechanism being defined on a higher level, lower-level processes influencing the host-pathogen interaction can be identified, modeled, and tested both quantitatively and qualitatively. This systems biology framework incorporates modeling to generate and test hypotheses, to perform virtual experiments, and to make experimentally verifiable predictions. Thereby it supports the unraveling of the mechanisms of tuberculosis infection.

Nuclear entry and exit of the NF-κB family of dimeric transcription factors plays an essential role in regulating cellular responses to inflammatory stress. The dynamics of this nuclear translocation can vary significantly within a cell population and may dramatically change e.g. upon drug exposure. Furthermore, there is significant heterogeneity in individual cell response upon stress signaling. In order to systematically determine factors that define NF-κB translocation dynamics, high-throughput screens that enable the analysis of dynamic NF-κB responses in individual cells in real time are essential. Thus far, only NF-κB downstream signaling responses of whole cell populations at the transcriptional level are in high-throughput mode. In this study, we developed a fully automated image analysis method to determine the time-course of NF-κB translocation in individual cells, suitable for high-throughput screenings in the context of compound screening and functional genomics. Two novel segmentation methods were used for defining the individual nuclear and cytoplasmic regions: watershed masked clustering (WMC) and best-fit ellipse of Voronoi cell (BEVC). The dynamic NFκB oscillatory response at the single cell and population level was coupled to automated extraction of 26 analogue translocation parameters including number of peaks, time to reach each peak, and amplitude of each peak. Our automated image analysis method was validated through a series of statistical tests demonstrating computational efficient and accurate NF-κB translocation dynamics quantification of our algorithm. Both pharmacological inhibition of NF-κB and short interfering RNAs targeting the inhibitor of NFκB, IκBα, demonstrated the ability of our method to identify compounds and genetic players that interfere with the nuclear transition of NF-κB.

The common carp is a candidate model system for immunology research. Using next-generation sequencing technology, we have generated a huge amount of sequence reads from the carp genome and transcriptome. Currently, our aim is to identify carp genes involved in the development of the innate immune response, particularly TIR domain-containing genes, from a preliminary genome assembly. To achieve this, we developed a comprehensive gene identification pipeline. This analysis allowed us to estimate that the carp has 39 TIR domain-containing transcript isoforms and genes.

We present a novel approach to modelling biological information using ontologies. The system interlinks three ontologies, comprising anatomical, developmental and taxonomical information, and includes instances of structures for different species. The framework is constructed for comparative analyses in the field of evolutionary development. We have applied the approach to the vertebrate heart and present four case studies of the functionality of the system, focusing on cross-species comparisons, developmental studies, physiological studies and 3D visualisation.

The hallmark of tuberculosis (TB) is the formation of granulomas, which are clusters of infected macrophages surrounded by additional macrophages, neutrophils and lymphocytes. Although it has long been thought that granulomas are beneficial for the host, there is evidence that mycobacteria also promote the formation of these structures. In this study, we aimed to identify new mycobacterial factors involved in the initial stages of granuloma formation. We exploited the zebrafish embryo Mycobacterium marinum infection model to study initiation of granuloma formation and developed an in vivo screen to select for random M. marinum mutants that were unable to induce granuloma formation efficiently. Upon screening 200 mutants, three mutants repeatedly initiated reduced granuloma formation. One of the mutants was found to be defective in the espL gene, which is located in the ESX-1 cluster. The ESX-1 cluster is disrupted in the Mycobacterium bovis BCG vaccine strain and encodes a specialized secretion system known to be important for granuloma formation and virulence. Although espL has not been implicated in protein secretion before, we observed a strong effect on the secretion of the ESX-1 substrates ESAT-6 and EspE. We conclude that our zebrafish embryo M. marinum screen is a useful tool to identify mycobacterial genes involved in the initial stages of granuloma formation and that we have identified a new component of the ESX-1 secretion system. We are confident that our approach will contribute to the knowledge of mycobacterial virulence and could be helpful for the development of new TB vaccines.

In this article we present a 3-D modeling study of cardiac development in the European pond turtle, Emys orbicularis (of the reptilian order Testudines). The study is aimed at elucidating the embryonic development of the horizontal septum in the ventricle and underscoring the importance of 3-D reconstructions in studying morphogenesis. Turtles possess one common ventricle, partly divided into three cava by a vertical and a horizontal septum, of which the embryonic origins have so far not been described. We used serial sectioning and computerized high-resolution 3-D reconstructions of different developmental stages to create a chronological overview of cardiogenesis, in order to study this process. This has yielded a new understanding of the development of the horizontal septum and (directly related) the looping of the heart tube. This looping is found to be markedly different from that in the human heart, with the turtle having two clear bends in the part of the heart tube leaving the primitive ventricle, as opposed to one in humans. It is this particular looping that is responsible for the formation of the horizontal septum. In addition to our findings on the ventricular septation this study has also yielded new insights into the developmental origins of the pulmonary vein. The 3-D reconstructions were built using our platform TDR-3-D base and enabled us to study the developmental processes in specific parts of the turtle heart separately and in three dimensions, over time. The complete 3-D reconstructions have been made available to the reader via internet using our 3-D model browser application, which allows interactive viewing of the models. The browser application can be found on bio-imaging.liacs.nl/galleries/emysorbicularis/TurtleGallery.html, along with additional images of both models and histological sections and animation sequences of the models. By allowing the reader to view the material in such an interactive way, we hope to make optimal use of the new 3-D reconstruction techniques and to engage the reader in a more direct manner.

Mining patterns of gene expression provides a crucial approach in discovering knowledge such as finding genetic networks that underpin the embryonic development. Analysis of mining results and evaluation of their relevance in the domain remains a major concern. In this paper we describe our explorative studies in support of solutions to facilitate the analysis and interpretation of mining results. In our particular case we describe a solution that is found in the extension of the Gene Expression Management System (GEMS), i.e. an integrative framework for spatio-temporal organization of gene expression patterns of zebrafish to a framework supporting data mining, data analysis and patterns interpretation As a proof of principle, the GEMS has been equipped with data mining functionality suitable for spatio-temporal tracking, thereby generating added value to the submission of data for data mining and analysis. The analysis of the genetic networks is based on the availability of domain ontologies which dynamically provides meaning to the discovered patterns of gene expression data. Combination of data mining with the already presently available capabilities of GEMS will significantly augment current data processing and functional analysis strategies.

microRNAs are short RNA fragments that have the capacity of regulating hundreds of target gene expression. Currently, due to lack of high-throughput experimental methods for miRNA target identification, a collection of computational target prediction approaches have been developed. However, these approaches deal with different features or factors are weighted differently resulting in diverse range of predictions. The prediction accuracy remains uncertain. In this paper, three commonly used target prediction algorithms are evaluated and further integrated using algorithm combination, ranking aggregation and Bayesian Network classification. Our results revealed that each individual prediction algorithm displays its advantages as was shown on different test data sets. Among different integration strategies, the application of Bayesian Network classifier on the features calculated from multiple prediction methods significantly improved target prediction accuracy.

Background: In the adult hippocampus, the granule cell layer of the dentate gyrus is a heterogeneous structure formed by neurons of different ages, morphologies and electrophysiological properties. Retroviral vectors have been extensively used to transduce cells of the granule cell layer and study their inherent properties in an intact brain environment. In addition, lentivirus-based vectors have been used to deliver transgenes to replicative and non-replicative cells as well, such as post mitotic neurons of the CNS. However, only few studies have been dedicated to address the applicability of these widespread used vectors to hippocampal cells in vivo. Therefore, the aim of this study was to extensively characterize the cell types that are effectively transduced in vivo by VSVg-pseudotyped lentivirus-based vectors in the hippocampus dentate gyrus., Results: In the present study we used Vesicular Stomatitis Virus G glycoprotein-pseudotyped lentivirual vectors to express EGFP from three different promoters in the mouse hippocampus. In contrast to lentiviral transduction of pyramidal cells in CA1, we identified sub-region specific differences in transgene expression in the granule cell layer of the dentate gyrus. Furthermore, we characterized the cell types transduced by these lentiviral vectors, showing that they target primarily neuronal progenitor cells and immature neurons present in the sub-granular zone and more immature layers of the granule cell layer., Conclusion: Our observations suggest the existence of intrinsic differences in the permissiveness to lentiviral transduction among various hippocampal cell types. In particular, we show for the first time that mature neurons of the granule cell layer do not express lentivirus-delivered transgenes, despite successful expression in other hippocampal cell types. Therefore, amongst hippocampal granule cells, only adult-generated neurons are target for lentivirus-mediated transgene delivery. These properties make lentiviral vectors excellent systems for overexpression or knockdown of genes in neuronal progenitor cells, immature neurons and adult-generated neurons of the mouse hippocampus in vivo.

Tcf/Lef HMG box transcription factors are nuclear effectors of the canonical Wnt signaling pathway, which function in cell fate specification. Lef1 is required for the development of tissues and organs that depend on epithelial mesenchymal interactions. Here, we report the effects of lef1 loss of function on early development in X. tropicalis. Depletion of lef1 affects gene expression already during gastrulation and results in abnormal differentiation of cells derived from ectoderm and mesoderm. At tail bud stages, the epidermis was devoid of ciliated cells and derivatives of the neural crest, e.g. melanocytes and cephalic ganglia were absent. In the Central Nervous System, nerve fibers were absent or underdeveloped. The development of the paraxial mesoderm was affected; intersomitic boundaries were not distinct and development of the hypaxial musculature was impaired. The development of the pronephros and pronephric ducts was disturbed. Most striking was the absence of blood flow in lef1 depleted embryos. Analysis of blood vessel marker genes demonstrated that lef1 is required for the development of the major blood vessels and the heart.

Basic research in pattern formation is concerned with the generation of phenotypes and tissues. It can therefore lead to new tools for medical research. These include phenotypic screening assays, applications in tissue engineering, as well as general advances in biomedical knowledge. Our aim here is to discuss this emerging field with special reference to tools based on zebrafish developmental biology. We describe phenotypic screening assays being developed in our own and other labs. Our assays involve: (i) systemic or local administration of a test compound or drug to zebrafish in vivo; (ii) the subsequent detection or "readout" of a defined phenotypic change. A positive readout may result from binding of the test compound to a molecular target involved in a developmental pathway. We present preliminary data on assays for compounds that modulate skeletal patterning, bone turnover, immune responses, inflammation and early-life stress. The assays use live zebrafish embryos and larvae as well as adult fish undergoing caudal fin regeneration. We describe proof-of-concept studies on the localised targeting of compounds into regeneration blastemas using microcarriers. Zebrafish are cheaper to maintain than rodents, produce large numbers of transparent eggs, and some zebrafish assays could be scaled-up into medium and high throughput screens. However, advances in automation and imaging are required. Zebrafish cannot replace mammalian models in the drug development pipeline. Nevertheless, they can provide a cost-effective bridge between cell-based assays and mammalian whole-organism models.

The Gene Expression Management System (GEMS) is a database system for patterns of gene expression. These patterns result from systematic whole-mount fluorescent in situ hybridization studies on zebrafish embryos. GEMS is an integrative platform that addresses one of the important challenges of developmental biology: how to integrate genetic data that underpin morphological changes during embryogenesis. Our motivation to build this system was by the need to be able to organize and compare multiple patterns of gene expression at tissue level. Integration with other developmental and biomolecular databases will further support our understanding of development. The GEMS operates in concert with a database containing a digital atlas of zebrafish embryo; this digital atlas of zebrafish development has been conceived prior to the expansion of the GEMS. The atlas contains 3D volume models of canonical stages of zebrafish development in which in each volume model element is annotated with an anatomical term. These terms are extracted from a formal anatomical ontology, i.e. the Developmental Anatomy Ontology of Zebrafish (DAOZ). In the GEMS, anatomical terms from this ontology together with terms from the Gene Ontology (GO) are also used to annotate patterns of gene expression and in this manner providing mechanisms for integration and retrieval. The annotations are the glue for integration of patterns of gene expression in GEMS as well as in other biomolecular databases. At the one hand, zebrafish anatomy terminology allows gene expression data within GEMS to be integrated with phenotypical data in the 3D atlas of zebrafish development. At the other hand, GO terms extend GEMS expression patterns integration to a wide range of bioinformatics resources.

From a differential display designed to isolate genes that are down-regulated upon differentiation of the central nervous system in Danio rerio embryos, we isolated d-asb11 (ankyrin repeat and suppressor of cytokine signaling box-containing protein 11). Knockdown of the d-Asb11 protein altered the expression of neural precursor genes sox2 and sox3 and resulted in an initial relative increase in proneural cell numbers. This was reflected by neurogenin1 expansion followed by premature neuronal differentiation, as demonstrated by HuC labeling and resulting in reduced size of the definitive neuronal compartment. Forced misexpression of d-asb11 was capable of ectopically inducing sox2 while it diminished or entirely abolished neurogenesis. Overexpression of d-Asb11 in both a pluripotent and a neural-committed progenitor cell line resulted in the stimulus-induced inhibition of terminal neuronal differentiation and enhanced proliferation. We conclude that d-Asb11 is a novel regulator of the neuronal progenitor compartment size by maintaining the neural precursors in the proliferating undifferentiated state possibly through the control of SoxB1 transcription factors.

We present a method and protocol for fluorescent in situ hybridization (FISH) in zebrafish embryos to enable three-dimensional imaging of patterns of gene expression using confocal laser scanning microscopy. We describe the development of our protocol and the processing workflow of the three-dimensional images from the confocal microscope. We refer to this protocol as zebraFISH. FISH is based on the use of tyramide signal amplification (TSA), which results in highly sensitive and very localized fluorescent staining. The zebraFISH protocol was extensively tested and here we present a panel of five probes for genes expressed in different tissues or single cells. FISH in combination with confocal laser scanning microscopy provides an excellent tool to generate three-dimensional images of patterns of gene expression. We propose that such three-dimensional images are suitable for building a repository of gene expression patterns, complementary to our previously published three-dimensional anatomical atlas of zebrafish development (bio-imaging.liacs.nl/). Our methodology for image processing of three-dimensional confocal images allows an analytical approach to the definition of gene expression domains based on the three-dimensional anatomical atlas.

Magnetic resonance microscopy (MRM) is an imaging modality that allows for noninvasive acquisition of high-resolution images in intact opaque animals. The zebrafish (Danio rerio) is an important model organism for the study of vertebrate biology. However, optical in vivo studies in zebrafish are restricted to very early developmental stages due to the opacity of the juvenile and adult stages. Application of high resolution MRM has not yet been explored in adult zebrafish. In this study we applied and optimized high resolution MRM methods to examine anatomical structures noninvasively in adult zebrafish. Clear morphological proton images were obtained by T(2)-weighted spin echo and rapid acquisition with rapid acquisition with relaxation enhancement (RARE) sequences which revealed many anatomical details in the entire intact zebrafish at a magnetic field strength of 9.4 T. In addition, in vivo imaging of adult zebrafish revealed sufficient anatomical details. To our knowledge this is the first report of the application of high resolution MRM to study detailed anatomical structures in adult zebrafish.

The zebrafish (Danio rerio) is an important model in evolutionary developmental biology, and its study is being revolutionized by the zebrafish genome project. Sequencing is at an advanced stage, but annotation is largely the result of in silico analyses. We have performed genomic annotation, comparative genomics, and transcriptional analysis using microarrays of the hox homeobox-containing transcription factors. These genes have important roles in specifying the body plan. Candidate sequences were located in version Z v 4 of the Ensembl genome database by TBLASTN searching with Danio and other vertebrate published Hox protein sequences. Homologies were confirmed by alignment with reference sequences, and by the relative position of genes along each cluster. RT-PCR using adult Tübingen cDNA was used to confirm annotations, to check the genomic sequence and to confirm expression in vivo. Our RT-PCR and microarray data show that all 49 hox genes are expressed in adult zebrafish. Significant expression for all known hox genes could be detected in our microarray analysis. We also find significant expression of hox 8 paralogs and hox b 7 a in the anti-sense direction. A novel gene, D. rerio hox b 13 a, was identified, and a preliminary characterization by in situ hybridization showed expression at 24 hpf at the tip of the developing tail. We are currently characterizing this gene at the functional level. We argue that the oligo design for microarrays can be greatly enhanced by the availability of genomic sequences.

The Mycobacterium marinum-zebrafish infection model was used in this study for analysis of a host transcriptome response to mycobacterium infection at the organismal level. RNA isolated from adult zebrafish that showed typical signs of fish tuberculosis due to a chronic progressive infection with M. marinum was compared with RNA from healthy fish in microarray analyses. Spotted oligonucleotide sets (designed by Sigma-Compugen and MWG) and Affymetrix GeneChips were used, in total comprising 45,465 zebrafish transcript annotations. Based on a detailed comparative analysis and quantitative reverse transcriptase-PCR analysis, we present a validated reference set of 159 genes whose regulation is strongly affected by mycobacterial infection in the three types of microarrays analyzed. Furthermore, we analyzed the separate datasets of the microarrays with special emphasis on the expression profiles of immune-related genes. Upregulated genes include many known components of the inflammatory response and several genes that have previously been implicated in the response to mycobacterial infections in cell cultures of other organisms. Different marker genes of the myeloid lineage that have been characterized in zebrafish also showed increased expression. Furthermore, the zebrafish homologs of many signal transduction genes with relationship to the immune response were induced by M. marinum infection. Future functional analysis of these genes may contribute to understanding the mechanisms of mycobacterial pathogenesis. Since a large group of genes linked to immune responses did not show altered expression in the infected animals, these results suggest specific responses in mycobacterium-induced disease.