Lymphatic endothelium stimulates melanoma metastasis and invasion via MMP14-dependent Notch3 and b1-integrin activation

Lymphatic invasion and lymph node metastasis correlate with poor clinical outcome in melanoma. However, the mechanisms of lymphatic dissemination in distant metastasis remain incompletely understood. We show here that exposure of expansively growing human WM852 melanoma cells, but not singly invasive Bowes cells, to lymphatic endothelial cells (LEC) in 3D co-culture facilitates melanoma distant organ metastasis in mice. To dissect the underlying molecular mechanisms, we established LEC co-cultures with different melanoma cells originating from primary tumors or metastases. Notably, the expansively growing metastatic melanoma cells adopted an invasively sprouting phenotype in 3D matrix that was dependent on MMP14, Notch3 and β1-integrin. Unexpectedly, MMP14 was necessary for LEC-induced Notch3 induction and coincident β1-integrin activation. Moreover, MMP14 and Notch3 were required for LEC-mediated metastasis of zebrafish xenografts. This study uncovers a unique mechanism whereby LEC contact promotes melanoma metastasis by inducing a reversible switch from 3D growth to invasively sprouting cell phenotype

Chemical composition of boundary layer aerosol over the Atlantic Ocean and at an Antarctic site

International audienceAerosol chemical composition was measured over the Atlantic Ocean in November?December 1999 and at the Finnish Antarctic research station Aboa in January 2000. The concentrations of all anthropogenic aerosol compounds decreased clearly from north to south. An anthropogenic influence was still evident in the middle of the tropical South Atlantic, background values were reached south of Cape Town. Chemical mass balance was calculated for high volume filter samples (Dp80% in the Southern Ocean, and 10% in most samples, also at Aboa. The correlation of biomass-burning-related aerosol components with 210Pb was very high compared with that between nss calcium and 210Pb which suggests that 210Pb is a better tracer for biomass burning than for Saharan dust. The ratio of the two clear tracers for biomass burning, nss potassium and oxalate, was different in European and in African samples, suggesting that this ratio could be used as an indicator of biomass burning type. The concentrations of continent-related particles decreased exponentially with the distance from Africa. The shortest half-value distance, ~100 km, was for nss calcium. The half-value distance of particles that are mainly in the submicron particles was ~700±200 km. The MSA to nss sulfate ratio, R, increased faster than MSA concentration with decreasing anthropogenic influence, indicating that the R increase could largely be explained by the decrease of anthropogenic sulfate

ErbB4 tyrosine kinase inhibition impairs neuromuscular development in zebrafish embryos

Tyrosine kinase inhibitors are widely used in the clinic, but limited information is available about their toxicity in developing organisms. Here, we tested the effect of tyrosine kinase inhibitors targeting the ErbB receptors for their effects on developing zebrafish ( Danio rerio) embryos. Embryos treated with wide-spectrum pan-ErbB inhibitors or erbb4a-targeting antisense oligonucleotides demonstrated reduced locomotion, reduced diameter of skeletal muscle fibers, reduced expression of muscle-specific genes, as well as reduced motoneuron length. The phenotypes in the skeletal muscle, as well as the defect in the motility, were rescued both by microinjection of human ERBB4 mRNA, and by transposon-mediated muscle-specific ERBB4 overexpression. The role of ErbB4 in regulating motility was further controlled by targeted mutation of the endogenous erbb4a locus in the zebrafish genome by CRISPR/Cas9. These observations demonstrate a potential for the ErbB tyrosine kinase inhibitors to induce neuromuscular toxicity in a developing organism via a mechanism involving inhibition of ErbB4 function.

An unbiased in vitro screen for activating epidermal growth factor receptor mutations

Cancer tissues harbor thousands of mutations, and a given oncogene may be mutated at hundreds of sites. Yet, only a few of these mutations have been functionally tested. Here, we describe an unbiased platform for the functional characterization of thousands of variants of a single receptor tyrosine kinase (RTK) gene in a single assay. Our in vitro screen for activating mutations (iSCREAM) platform enabled rapid analysis of mutations conferring gain-of-function RTK activity promoting clonal growth. The screening strategy included a somatic model of cancer evolution and utilized a library of 7,216 randomly mutated epidermal growth factor receptor (EGFR) single-nucleotide variants, that were tested in murine lymphoid Ba/F3 cells. These cells depend on exogenous interleukin-3 (IL-3) for growth, but this dependency can be compensated by ectopic EGFR overexpression, enabling selection for gain-of-function EGFR mutants. Analysis of the enriched mutants revealed EGFR A702V, a novel activating variant that structurally stabilized the EGFR kinase dimer interface and conferred sensitivity to kinase inhibition by afatinib. As proof of concept for our approach, we recapitulated clinical observations and identified the EGFR L858R as the major enriched EGFR variant. Altogether iSCREAM enabled robust enrichment of 21 variants from a total of 7,216 EGFR mutations. These findings indicate the power of this screening platform for unbiased identification of activating RTK variants that are enriched under selection pressure in a model of cancer heterogeneity and evolution

Understanding atmospheric organic aerosols via factor analysis of aerosol mass spectrometry: a review

Organic species are an important but poorly characterized constituent of airborne particulate matter. A quantitative understanding of the organic fraction of particles (organic aerosol, OA) is necessary to reduce some of the largest uncertainties that confound the assessment of the radiative forcing of climate and air quality management policies. In recent years, aerosol mass spectrometry has been increasingly relied upon for highly time-resolved characterization of OA chemistry and for elucidation of aerosol sources and lifecycle processes. Aerodyne aerosol mass spectrometers (AMS) are particularly widely used, because of their ability to quantitatively characterize the size-resolved composition of submicron particles (PM1). AMS report the bulk composition and temporal variations of OA in the form of ensemble mass spectra (MS) acquired over short time intervals. Because each MS represents the linear superposition of the spectra of individual components weighed by their concentrations, multivariate factor analysis of the MS matrix has proved effective at retrieving OA factors that offer a quantitative and simplified description of the thousands of individual organic species. The sum of the factors accounts for nearly 100% of the OA mass and each individual factor typically corresponds to a large group of OA constituents with similar chemical composition and temporal behavior that are characteristic of different sources and/or atmospheric processes. The application of this technique in aerosol mass spectrometry has grown rapidly in the last six years. Here we review multivariate factor analysis techniques applied to AMS and other aerosol mass spectrometers, and summarize key findings from field observations. Results that provide valuable information about aerosol sources and, in particular, secondary OA evolution on regional and global scales are highlighted. Advanced methods, for example a-priori constraints on factor mass spectra and the application of factor analysis to combined aerosol and gas phase data are discussed. Integrated analysis of worldwide OA factors is used to present a holistic regional and global description of OA. Finally, different ways in which OA factors can constrain global and regional models are discussed

A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Belis et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management

Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

<p>Abstract</p> <p>Background</p> <p>Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of <it>k</it>-partite graphs. These graphs contain <it>k </it>different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type.</p> <p>Results</p> <p>Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a <it>k</it>-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted <it>k</it>-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2.</p> <p>Conclusions</p> <p>In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy <it>k</it>-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.</p

SHANK3 conformation regulates direct actin binding and crosstalk with Rap1 signaling

Actin-rich cellular protrusions direct versatile biological processes from cancer cell invasion to dendritic spine development. The stability, morphology, and specific biological functions of these protrusions are regulated by crosstalk between three main signaling axes: integrins, actin regulators, and small guanosine triphosphatases (GTPases). SHANK3 is a multifunctional scaffold protein, interacting with several actin-binding proteins and a well-established autism risk gene. Recently, SHANK3 was demonstrated to sequester integrin-activating small GTPases Rap1 and R-Ras to inhibit integrin activity via its Shank/ProSAP N-terminal (SPN) domain. Here, we demonstrate that, in addition to scaffolding actin regulators and actin-binding proteins, SHANK3 interacts directly with actin through its SPN domain. Molecular simulations and targeted mutagenesis of the SPN-ankyrin repeat region (ARR) interface reveal that actin binding is inhibited by an intramolecular closed conformation of SHANK3, where the adjacent ARR domain covers the actin-binding interface of the SPN domain. Actin and Rap1 compete with each other for binding to SHANK3, and mutation of SHANK3, resulting in reduced actin binding, augments inhibition of Rap1-mediated integrin activity. This dynamic crosstalk has functional implications for cell morphology and integrin activity in cancer cells. In addition, SHANK3-actin interaction regulates dendritic spine morphology in neurons and autism-linked phenotypes in vivo

Local and regional components of aerosol in a heavily trafficked street canyon in central London derived from PMF and cluster analysis of single-particle ATOFMS spectra.

Positive matrix factorization (PMF) has been applied to single particle ATOFMS spectra collected on a six lane heavily trafficked road in central London (Marylebone Road), which well represents an urban street canyon. PMF analysis successfully extracted 11 factors from mass spectra of about 700,000 particles as a complement to information on particle types (from K-means cluster analysis). The factors were associated with specific sources and represent the contribution of different traffic related components (i.e., lubricating oils, fresh elemental carbon, organonitrogen and aromatic compounds), secondary aerosol locally produced (i.e., nitrate, oxidized organic aerosol and oxidized organonitrogen compounds), urban background together with regional transport (aged elemental carbon and ammonium) and fresh sea spray. An important result from this study is the evidence that rapid chemical processes occur in the street canyon with production of secondary particles from road traffic emissions. These locally generated particles, together with aging processes, dramatically affected aerosol composition producing internally mixed particles. These processes may become important with stagnant air conditions and in countries where gasoline vehicles are predominant and need to be considered when quantifying the impact of traffic emissions.This is the author accepted manuscript. The final version is available via ACS at http://pubs.acs.org/doi/abs/10.1021/es506249z

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing