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

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

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

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

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

Application of the bacteriophage Mu-driven system for the integration/amplification of target genes in the chromosomes of engineered Gram-negative bacteria—mini review

The advantages of phage Mu transposition-based systems for the chromosomal editing of plasmid-less strains are reviewed. The cis and trans requirements for Mu phage-mediated transposition, which include the L/R ends of the Mu DNA, the transposition factors MuA and MuB, and the cis/trans functioning of the E element as an enhancer, are presented. Mini-Mu(LR)/(LER) units are Mu derivatives that lack most of the Mu genes but contain the L/R ends or a properly arranged E element in cis to the L/R ends. The dual-component system, which consists of an integrative plasmid with a mini-Mu and an easily eliminated helper plasmid encoding inducible transposition factors, is described in detail as a tool for the integration/amplification of recombinant DNAs. This chromosomal editing method is based on replicative transposition through the formation of a cointegrate that can be resolved in a recombination-dependent manner. (E-plus)- or (E-minus)-helpers that differ in the presence of the trans-acting E element are used to achieve the proper mini-Mu transposition intensity. The systems that have been developed for the construction of stably maintained mini-Mu multi-integrant strains of Escherichia coli and Methylophilus methylotrophus are described. A novel integration/amplification/fixation strategy is proposed for consecutive independent replicative transpositions of different mini-Mu(LER) units with “excisable” E elements in methylotrophic cells

An in vitro comparison of subjective image quality of panoramic views acquired via 2D or 3D imaging.

Item does not contain fulltextOBJECTIVES: The objective of this study is to compare subjective image quality and diagnostic validity of cone-beam CT (CBCT) panoramic reformatting with digital panoramic radiographs. MATERIALS AND METHODS: Four dry human skulls and two formalin-fixed human heads were scanned using nine different CBCTs, one multi-slice CT (MSCT) and one standard digital panoramic device. Panoramic views were generated from CBCTs in four slice thicknesses. Seven observers scored image quality and visibility of 14 anatomical structures. Four observers repeated the observation after 4 weeks. RESULTS: Digital panoramic radiographs showed significantly better visualization of anatomical structures except for the condyle. Statistical analysis of image quality showed that the 3D imaging modalities (CBCTs and MSCT) were 7.3 times more likely to receive poor scores than the 2D modality. Yet, image quality from NewTom VGi(R) and 3D Accuitomo 170(R) was almost equivalent to that of digital panoramic radiographs with respective odds ratio estimates of 1.2 and 1.6 at 95% Wald confidence limits. A substantial overall agreement amongst observers was found. Intra-observer agreement was moderate to substantial. CONCLUSIONS: While 2D-panoramic images are significantly better for subjective diagnosis, 2/3 of the 3D-reformatted panoramic images are moderate or good for diagnostic purposes. CLINICAL RELEVANCE: Panoramic reformattings from particular CBCTs are comparable to digital panoramic images concerning the overall image quality and visualization of anatomical structures. This clinically implies that a 3D-derived panoramic view can be generated for diagnosis with a recommended 20-mm slice thickness, if CBCT data is a priori available for other purposes.1 januari 201