Ozone-primed neutrophils promote early steps of tumour cell metastasis to lungs by enhancing their NET production.

BACKGROUND: Air pollution, including particulates and gazes such as ozone (O3), is detrimental for patient's health and has repeatedly been correlated to increased morbidity and mortality in industrialised countries. Although studies have described a link between ambient particulate matter and increased lung cancer morbidity, no direct relation has yet been established between O3 exposure and metastatic dissemination to lungs. OBJECTIVES: To outline the mechanisms through which pulmonary O3 exposure modulates metastasis kinetics in an experimental mouse model of O3 exposure. METHODS: Metastatic responses to pulmonary O3 exposure were assessed using a reliable experimental mouse model of concomitant pulmonary O3 exposure and tumour cell injection. Roles of neutrophils in O3-induced lung metastasis were highlighted using blocking anti-Ly6G antibodies; moreover, the implication of neutrophil extracellular traps (NETs) in metastatic processes was evaluated using MRP8cre-Pad4lox/lox mice or by treating mice with DNase I. RESULTS: Pulmonary O3 exposure strongly facilitates the establishment of lung metastasis by (1) Inducing a pulmonary injury and neutrophilic inflammation, (2) Influencing very early steps of metastasis, (3) Priming neutrophils' phenotype to release NETs that favour tumour cell colonisation in lungs. The ability of O3-primed neutrophils to enhance lung colonisation by tumour cells was proven after their adoptive transfer in Balb/c mice unexposed to O3. CONCLUSIONS: Pulmonary neutrophils induced by O3 promote metastatic dissemination to lungs by producing NETs. These findings open new perspectives to improve treatment and prevention strategies in patients affected by metastatic diseases.

[A Massive Open Oneline Course (MOOC) on pratical histology: A goal, a tool, a large public ! Return on a first experience]. / Un « Massive Open Online Course ¼ (MOOC) sur des travaux pratiques en histologie : un objectif, un outil, un public varié ! Retour sur une première expérience.

A goal ! The MOOC entitled "Introduction to Histology, A Human Tissue Exploration" correspond to our vision of the practice of General Histology, which is based on the ability to diagnose 5 families of biological tissues. Ultimately, participants must be able to recognize the different types of cells and all the surrounding elements in order to understand how they organize themselves to form tissues with specific functions. A tool ! This know-how is based on reasoning from observations of microscopic structures. Learners are therefore invited to manipulate a virtual microscope to explore biological samples on histological slides digitized. Annotations, comments, drawings or photos are associated with landmarks that enrich the study of these histological sections. A target audience ! Two educational paths allow deepening the subject in a different way and thus matching the goals or motivations of each one. After a first year of experience, usage statistics and surveys of our learners show that the MOOC Histo has allowed each of them to find an interest and federate a community of motivated learners.

Collaborative analysis of multi-gigapixel imaging data using Cytomine.

MOTIVATION: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. RESULTS: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications. AVAILABILITY AND IMPLEMENTATION: Cytomine (http://www.cytomine.be/) is freely available under an open-source license from http://github.com/cytomine/ A documentation wiki (http://doc.cytomine.be) and a demo server (http://demo.cytomine.be) are also available. CONTACT: info@cytomine.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of BRCA1-related molecular features and microRNAs as prognostic factors for triple negative breast cancers.

BACKGROUND: The BRCA1 gene plays a key role in triple negative breast cancers (TNBCs), in which its expression can be lost by multiple mechanisms: germinal mutation followed by deletion of the second allele; negative regulation by promoter methylation; or miRNA-mediated silencing. This study aimed to establish a correlation among the BRCA1-related molecular parameters, tumor characteristics and clinical follow-up of patients to find new prognostic factors. METHODS: BRCA1 protein and mRNA expression was quantified in situ in the TNBCs of 69 patients. BRCA1 promoter methylation status was checked, as well as cytokeratin 5/6 expression. Maintenance of expressed BRCA1 protein interaction with BARD1 was quantified, as a marker of BRCA1 functionality, and the tumor expression profiles of 27 microRNAs were determined. RESULTS: miR-548c-5p was emphasized as a new independent prognostic factor in TNBC. A combination of the tumoral expression of miR-548c and three other known prognostic parameters (tumor size, lymph node invasion and CK 5/6 expression status) allowed for relapse prediction by logistic regression with an area under the curve (AUC) = 0.96. BRCA1 mRNA and protein in situ expression, as well as the amount of BRCA1 ligated to BARD1 in the tumor, lacked any associations with patient outcomes, likely due to high intratumoral heterogeneity, and thus could not be used for clinical purposes. CONCLUSIONS: In situ BRCA1-related expression parameters could be used for clinical purposes at the time of diagnosis. In contrast, miR-548c-5p showed a promising potential as a prognostic factor in TNBC.

Structural determinants of specificity and catalytic mechanism in mammalian 25-kDa thiamine triphosphatase.

BACKGROUND: Thiamine triphosphate (ThTP) is present in most organisms and might be involved in intracellular signaling. In mammalian cells, the cytosolic ThTP level is controlled by a specific thiamine triphosphatase (ThTPase), belonging to the CYTH superfamily of proteins. CYTH proteins are present in all superkingdoms of life and act on various triphosphorylated substrates. METHODS: Using crystallography, mass spectrometry and mutational analysis, we identified the key structural determinants of the high specificity and catalytic efficiency of mammalian ThTPase. RESULTS: Triphosphate binding requires three conserved arginines while the catalytic mechanism relies on an unusual lysine-tyrosine dyad. By docking of the ThTP molecule in the active site, we found that Trp-53 should interact with the thiazole part of the substrate molecule, thus playing a key role in substrate recognition and specificity. Sea anemone and zebrafish CYTH proteins, which retain the corresponding Trp residue, are also specific ThTPases. Surprisingly, the whole chromosome region containing the ThTPase gene is lost in birds. CONCLUSIONS: The specificity for ThTP is linked to a stacking interaction between the thiazole heterocycle of thiamine and a tryptophan residue. The latter likely plays a key role in the secondary acquisition of ThTPase activity in early metazoan CYTH enzymes, in the lineage leading from cnidarians to mammals. GENERAL SIGNIFICANCE: We show that ThTPase activity is not restricted to mammals as previously thought but is an acquisition of early metazoans. This, and the identification of critically important residues, allows us to draw an evolutionary perspective of the CYTH family of proteins.

Evaluation of CellSolutions BestPrep® automated thin-layer liquid-based cytology Papanicolaou slide preparation and BestCyte® cell sorter imaging system.

OBJECTIVE: A double-blind study was conducted to compare the performance of the new BestPrep® (CellSolutions) liquid-based thin-layer Papanicolaou (Pap) test with ThinPrep® (Hologic). STUDY DESIGN: Samples from the study patients (n = 105) were collected twice in the same encounter with the ThinPrep sample always taken first and the BestPrep sample collected second. Slides were prepared according to both manufacturers' protocols and evaluated using manual microscopic review and the BestCyte® cell sorter imaging system (CellSolutions). Diagnostic truth for each case was determined by independent manual review of both slides by multiple pathologists and histology when available. The presence of atypical squamous cells of undetermined significance was the threshold for positive for sensitivity and specificity calculations. RESULTS: BestPrep and ThinPrep, by manual review, had sensitivities for high-grade squamous intraepithelial lesion (HSIL) cases of 100 and 95.6%, respectively. Using the BestCyte cell sorter, both had 100% sensitivity. For the same HSIL cases, the digene HC2 high-risk human papillomavirus DNA test had sensitivities of 100% (BestPrep) and 95.6% (ThinPrep). Specificities were 71.4% (BestPrep) and 54.8% (ThinPrep). CONCLUSIONS: BestPrep was equivalent to ThinPrep for manual review even though BestPrep was always the second sample collected. The BestCyte cell sorter provides a practical alternative to manual review for both BestPrep and ThinPrep slides.

Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species.

Detecting skeletal or bone-related deformities in model and aquaculture fish is vital for numerous biomedical studies. In biomedical research, model fish with bone-related disorders are potential indicators of various chemically induced toxins in their environment or poor dietary conditions. In aquaculture, skeletal deformities are affecting fish health, and economic losses are incurred by fish farmers. This survey paper focuses on showcasing the cutting-edge image analysis tools and techniques based on artificial intelligence that are currently applied in the analysis of bone-related deformities in aquaculture and model fish. These methods and tools play a significant role in improving research by automating various aspects of the analysis. This paper also sheds light on some of the hurdles faced when dealing with high-content bioimages and explores potential solutions to overcome these challenges.

Validating instructional design and predicting student performance in histology education: Using machine learning via virtual microscopy.

As a part of modern technological environments, virtual microscopy enriches histological learning, with support from large institutional investments. However, existing literature does not supply empirical evidence of its role in improving pedagogy. Virtual microscopy provides fresh opportunities for investigating user behavior during the histology learning process, through digitized histological slides. This study establishes how students' perceptions and user behavior data can be processed and analyzed using machine learning algorithms. These also provide predictive data called learning analytics that enable predicting students' performance and behavior favorable for academic success. This information can be interpreted and used for validating instructional designs. Data on the perceptions, performances, and user behavior of 552 students enrolled in a histology course were collected from the virtual microscope, Cytomine®. These data were analyzed using an ensemble of machine learning algorithms, the extra-tree regression method, and predictive statistics. The predictive algorithms identified the most pertinent histological slides and descriptive tags, alongside 10 types of student behavior conducive to academic success. We used these data to validate our instructional design, and align the educational purpose, learning outcomes, and evaluation methods of digitized histological slides on Cytomine®. This model also predicts students' examination scores, with an error margin of <0.5 out of 20 points. The results empirically demonstrate the value of a digital learning environment for both students and teachers of histology.

Discovery and biochemical characterisation of four novel biomarkers for osteoarthritis.

OBJECTIVE: Knee osteoarthritis (OA) is a heterogeneous, complex joint pathology of unknown aetiology. Biomarkers have been widely used to investigate OA but currently available biomarkers lack specificity and sensitivity. Therefore, novel biomarkers are needed to better understand the pathophysiological processes of OA initiation and progression. METHODS: Surface enhanced laser desorption/ionisation-time of flight-mass spectrometry proteomic technique was used to analyse protein expression levels in 284 serum samples from patients with knee OA classified according to Kellgren and Lawrence (K&L) score (0-4). OA serum samples were also compared to serum samples provided by healthy individuals (negative control subjects; NC; n=36) and rheumatoid arthritis (RA) patients (n=25). Proteins that gave similar signal in all K&L groups of OA patients were ignored, whereas proteins with increased or decreased levels of expression were selected for further studies. RESULTS: Two proteins were found to be expressed at higher levels in sera of OA patients at all four K&L scores compared to NC and RA, and were identified as V65 vitronectin fragment and C3fpeptide. Of the two remaining proteins, one showed increased expression (unknown protein at m/z of 3762) and the other (identified as connective tissue-activating peptide III protein) was decreased in K&L scores >2 subsets compared to NC, RA and K&L scores 0 or 1 subsets. CONCLUSION: The authors detected four unexpected biomarkers (V65 vitronectin fragment, C3f peptide, CTAP-III and m/z 3762 protein) that could be relevant in the pathophysiological process of OA as having significant correlation with parameters reflecting local inflammation and bone remodelling, as well as decrease in cartilage turnover.

High-density lipoprotein proteome dynamics in human endotoxemia.

BACKGROUND: A large variety of proteins involved in inflammation, coagulation, lipid-oxidation and lipid metabolism have been associated with high-density lipoprotein (HDL) and it is anticipated that changes in the HDL proteome have implications for the multiple functions of HDL. Here, SELDI-TOF mass spectrometry (MS) was used to study the dynamic changes of HDL protein composition in a human experimental low-dose endotoxemia model. Ten healthy men with low HDL cholesterol (0.7+/-0.1 mmol/L) and 10 men with high HDL cholesterol levels (1.9+/-0.4 mmol/L) were challenged with endotoxin (LPS) intravenously (1 ng/kg bodyweight). We previously showed that subjects with low HDL cholesterol are more susceptible to an inflammatory challenge. The current study tested the hypothesis that this discrepancy may be related to differences in the HDL proteome. RESULTS: Plasma drawn at 7 time-points over a 24 hour time period after LPS challenge was used for direct capture of HDL using antibodies against apolipoprotein A-I followed by subsequent SELDI-TOF MS profiling. Upon LPS administration, profound changes in 21 markers (adjusted p-value < 0.05) were observed in the proteome in both study groups. These changes were observed 1 hour after LPS infusion and sustained up to 24 hours, but unexpectedly were not different between the 2 study groups. Hierarchical clustering of the protein spectra at all time points of all individuals revealed 3 distinct clusters, which were largely independent of baseline HDL cholesterol levels but correlated with paraoxonase 1 activity. The acute phase protein serum amyloid A-1/2 (SAA-1/2) was clearly upregulated after LPS infusion in both groups and comprised both native and N-terminal truncated variants that were identified by two-dimensional gel electrophoresis and mass spectrometry. Individuals of one of the clusters were distinguished by a lower SAA-1/2 response after LPS challenge and a delayed time-response of the truncated variants. CONCLUSIONS: This study shows that the semi-quantitative differences in the HDL proteome as assessed by SELDI-TOF MS cannot explain why subjects with low HDL cholesterol are more susceptible to a challenge with LPS than those with high HDL cholesterol. Instead the results indicate that hierarchical clustering could be useful to predict HDL functionality in acute phase responses towards LPS.

P2X1 ion channels promote neutrophil chemotaxis through Rho kinase activation.

ATP, released at the leading edge of migrating neutrophils, amplifies chemotactic signals. The aim of our study was to investigate whether neutrophils express ATP-gated P2X(1) ion channels and whether these channels could play a role in chemotaxis. Whole-cell patch clamp experiments showed rapidly desensitizing currents in both human and mouse neutrophils stimulated with P2X(1) agonists, alphabeta-methylene ATP (alphabetaMeATP) and betagammaMeATP. These currents were strongly impaired or absent in neutrophils from P2X(1)(-/-) mice. In Boyden chamber assays, alphabetaMeATP provoked chemokinesis and enhanced formylated peptide- and IL-8-induced chemotaxis of human neutrophils. This agonist similarly increased W-peptide-induced chemotaxis of wild-type mouse neutrophils, whereas it had no effect on P2X(1)(-/-) neutrophils. In human as in mouse neutrophils, alphabetaMeATP selectively activated the small RhoGTPase RhoA that caused reversible myosin L chain phosphorylation. Moreover, the alphabetaMeATP-elicited neutrophil movements were prevented by the two Rho kinase inhibitors, Y27632 and H1152. In a gradient of W-peptide, P2X(1)(-/-) neutrophils migrated with reduced speed and displayed impaired trailing edge retraction. Finally, neutrophil recruitment in mouse peritoneum upon Escherichia coli injection was enhanced in wild-type mice treated with alphabetaMeATP, whereas it was significantly impaired in the P2X(1)(-/-) mice. Thus, activation of P2X(1) ion channels by ATP promotes neutrophil chemotaxis, a process involving Rho kinase-dependent actomyosin-mediated contraction at the cell rear. These ion channels may therefore play a significant role in host defense and inflammation.

Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology.

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.

Bioimage analysis workflows: community resources to navigate through a complex ecosystem.

Workflows are the keystone of bioimage analysis, and the NEUBIAS (Network of European BioImage AnalystS) community is trying to gather the actors of this field and organize the information around them.  One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows and associated resources. In this paper we want to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows.  The emergence of "big data" in bioimaging and resource-intensive analysis algorithms make local data storage and computing solutions a limiting factor. At the same time, the need for data sharing with collaborators and a general shift towards remote work, have created new challenges and avenues for the execution and sharing of bioimage analysis workflows. These challenges are to reproducibly run workflows in remote environments, in particular when their components come from different software packages, but also to document them and link their parameters and results by following the FAIR principles (Findable, Accessible, Interoperable, Reusable) to foster open and reproducible science. In this opinion paper, we focus on giving some directions to the reader to tackle these challenges and navigate through this complex ecosystem, in order to find and use workflows, and to compare workflows addressing the same problem. We also discuss tools to run workflows in the cloud and on High Performance Computing resources, and suggest ways to make these workflows FAIR.

Oligodendrocyte development and myelinogenesis are not impaired by high concentrations of phenylalanine or its metabolites.

Phenylketonuria (PKU) is a metabolic genetic disease characterized by deficient phenylalanine hydroxylase (PAH) enzymatic activity. Brain hypomyelination has been reported in untreated patients, but its mechanism remains unclear. We therefore investigated the influence of phenylalanine (Phe), phenylpyruvate (PP), and phenylacetate (PA) on oligodendrocytes. We first showed in a mouse model of PKU that the number of oligodendrocytes is not different in corpus callosum sections from adult mutants or from control brains. Then, using enriched oligodendroglial cultures, we detected no cytotoxic effect of high concentrations of Phe, PP, or PA. Finally, we analyzed the impact of Phe, PP, and PA on the myelination process in myelinating cocultures using both an in vitro index of myelination, based on activation of the myelin basic protein (MBP) promoter, and the direct quantification of myelin sheaths by both optical measurement and a bioinformatics method. None of these parameters was affected by the increased levels of Phe or its derivatives. Taken together, our data demonstrate that high levels of Phe, such as in PKU, are unlikely to directly induce brain hypomyelination, suggesting involvement of alternative mechanisms in this myelination defect.

BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows.

Image analysis is key to extracting quantitative information from scientific microscopy images, but the methods involved are now often so refined that they can no longer be unambiguously described by written protocols. We introduce BIAFLOWS, an open-source web tool enabling to reproducibly deploy and benchmark bioimage analysis workflows coming from any software ecosystem. A curated instance of BIAFLOWS populated with 34 image analysis workflows and 15 microscopy image datasets recapitulating common bioimage analysis problems is available online. The workflows can be launched and assessed remotely by comparing their performance visually and according to standard benchmark metrics. We illustrated these features by comparing seven nuclei segmentation workflows, including deep-learning methods. BIAFLOWS enables to benchmark and share bioimage analysis workflows, hence safeguarding research results and promoting high-quality standards in image analysis. The platform is thoroughly documented and ready to gather annotated microscopy datasets and workflows contributed by the bioimaging community.

Biomarker discovery in asthma-related inflammation and remodeling.

Asthma is a complex inflammatory disease of airways. A network of reciprocal interactions between inflammatory cells, peptidic mediators, extracellular matrix components, and proteases is thought to be involved in the installation and maintenance of asthma-related airway inflammation and remodeling. To date, new proteic mediators displaying significant activity in the pathophysiology of asthma are still to be unveiled. The main objective of this study was to uncover potential target proteins by using surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) on lung samples from mouse models of allergen-induced airway inflammation and remodeling. In this model, we pointed out several protein or peptide peaks that were preferentially expressed in diseased mice as compared to controls. We report the identification of different five proteins: found inflammatory zone 1 or RELM alpha (FIZZ-1), calcyclin (S100A6), clara cell secretory protein 10 (CC10), Ubiquitin, and Histone H4.

Open Practices and Resources for Collaborative Digital Pathology.

In this paper, we describe open practices and open resources in the field of digital pathology with a specific focus on approaches that ease collaboration in research and education settings. Our review includes open access journals and open peer review, open-source software (libraries, desktop tools, and web applications), and open access collections. We illustrate applications and discuss current limitations and perspectives.

Cytomine: Toward an Open and Collaborative Software Platform for Digital Pathology Bridged to Molecular Investigations.

PURPOSE: Digital histology is being increasingly used in research and clinical applications. In parallel, new tissue imaging methods (e.g., imaging mass spectrometry) are currently regarded as very promising approaches for better molecular diagnosis in pathology. However, these new data sources are still often underexploited because of the lack of collaborative software to share and correlate information for multimodal analysis. EXPERIMENTAL DESIGN: The open science paradigm is followed to develop new features in the web-based Cytomine software to support next-generation digital pathology bridged to molecular investigations. RESULTS: New open-source developments allow to explore whole-slide classical histology with Matrix Assisted Laser Desorption Ionisation (MALDI) imaging and to support preprocessing for biomarker discovery using laser microdissection-based microproteomics. CONCLUSIONS AND CLINICAL RELEVANCE: The updated version of Cytomine is the first open and web-based tool to enable sharing data from classical histology, molecular imaging, and cell counting for proteomics preprocessing. It holds good promise to fulfill imminent needs in molecular histopathology.

A novel formulation of inhaled doxycycline reduces allergen-induced inflammation, hyperresponsiveness and remodeling by matrix metalloproteinases and cytokines modulation in a mouse model of asthma.

BACKGROUND: In this study, we assess the effectiveness of inhaled doxycycline, a tetracycline antibiotic displaying matrix metalloproteinases (MMP) inhibitory effects to prevent allergen-induced inflammation, hyperresponsiveness and remodeling. MMPs play key roles in the complex cascade of events leading to asthmatic phenotype. METHODS: Doxycycline was administered by aerosols by the mean of a novel formulation as a complex with hydroxypropyl-gamma-cyclodextrin (HP-gamma-CD) used as an excipient. BALB/c mice (n=16-24 in each group) were sensitized and exposed to aerosolized ovalbumin (OVA) from day 21 to 27 (short-term exposure protocol) or 5 days/odd weeks from day 22 to 96 (long-term exposure protocol). RESULTS: In the short-term exposure model, inhaled doxycycline decreased allergen-induced eosinophilic inflammation in bronchoalveolar lavage (BAL) and in peribronchial areas, as well as airway hyperresponsiveness. In lung tissue, exposure to doxycycline via inhaled route induced a fourfold increase in IL-10 levels, a twofold decrease in IL-5, IL-13 levels and diminished MMP-related proteolysis and the proportion of activated MMP-9 as compared to placebo. In the long-term exposure model, inhaled doxycycline significantly decreased the extent of glandular hyperplasia, airway wall thickening, smooth muscle hyperplasia and subepithelial collagen deposition which are well recognized features of airway remodeling. CONCLUSION: Doxycycline administered by aerosols decreases the allergen-induced airway inflammation and hyperresponsiveness and inhibits the development of bronchial remodeling in a mouse model of asthma by modulation of cytokines production and MMP activity.

Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach.

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research.