A database of flavivirus RNA structures with a search algorithm for pseudoknots and triple base interactions.

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.

Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network.

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.

Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations.

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.

Automated image analysis system for studying cardiotoxicity in human pluripotent stem cell-Derived cardiomyocytes.

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.

Hierarchical classification strategy for Phenotype extraction from epidermal growth factor receptor endocytosis screening.

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.

Modeling biological gradient formation: combining partial differential equations and Petri nets.

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.

Underlying molecular mechanisms of DIO2 susceptibility in symptomatic osteoarthritis.

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 (ß=4.96; p=0.0016). This effect was enhanced and appeared driven specifically by DIO2 rs225014 risk allele carriers (ß=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.

The residence time of focal adhesion kinase (FAK) and paxillin at focal adhesions in renal epithelial cells is determined by adhesion size, strength and life cycle status.

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.

Multiple graphical views for automatically generating SQL for the MycoDiversity DB; making fungal biodiversity studies accessible.

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.

Embryonic and larval zebrafish models for the discovery of new bioactive compounds against tuberculosis.

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.

Deep learning-based classification of early-stage mycosis fungoides and benign inflammatory dermatoses on hematoxylin and eosin-stained whole-slide images: a retrospective, proof-of-concept study.

The diagnosis of early-stage mycosis fungoides (MF) is challenging due to shared clinical and histopathological features with benign inflammatory dermatoses (BIDs). 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 BIDs using a unique dataset of 924 hematoxylin and eosin-stained whole-slide images from skin biopsies, including 233 early-stage MF and 353 BID patients. All MF patients were diagnosed after clinicopathological correlation. The classification accuracy of weakly-supervised DL models was benchmarked against three expert pathologists. The highest performance on a temporal test set was at 200x magnification (0.25 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 three expert-pathologists. Most (63.5%) attention heatmaps corresponded well with the pathologists' region-of-interest. Considering the difficulty of the MF versus BID 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.

Outlier detection using iterative adaptive mini-minimum spanning tree generation with applications on medical data.

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.

Image Synthesis and Modified BlendMask Instance Segmentation for Automated Nanoparticle Phenotyping.

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.

The novel gene asb11: a regulator of the size of the neural progenitor compartment.

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.

Systems microscopy approaches to understand cancer cell migration and metastasis.

Cell migration is essential in a number of processes, including wound healing, angiogenesis and cancer metastasis. Especially, invasion of cancer cells in the surrounding tissue is a crucial step that requires increased cell motility. Cell migration is a well-orchestrated process that involves the continuous formation and disassembly of matrix adhesions. Those structural anchor points interact with the extra-cellular matrix and also participate in adhesion-dependent signalling. Although these processes are essential for cancer metastasis, little is known about the molecular mechanisms that regulate adhesion dynamics during tumour cell migration. In this review, we provide an overview of recent advanced imaging strategies together with quantitative image analysis that can be implemented to understand the dynamics of matrix adhesions and its molecular components in relation to tumour cell migration. This dynamic cell imaging together with multiparametric image analysis will help in understanding the molecular mechanisms that define cancer cell migration.

A Novel Function of TLR2 and MyD88 in the Regulation of Leukocyte Cell Migration Behavior During Wounding in Zebrafish Larvae.

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.

Neural networks for increased accuracy of allergenic pollen monitoring.

Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

Fungal metabarcoding data integration framework for the MycoDiversity DataBase (MDDB).

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.

Anti-tuberculosis effect of isoniazid scales accurately from zebrafish to humans.

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.

Lentivirus-mediated transgene delivery to the hippocampus reveals sub-field specific differences in expression.

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.