SPROUTY-2 represses the epithelial phenotype of colon carcinoma cells via upregulation of ZEB1 mediated by ETS1 and miR-200/miR-150

SPROUTY-2 (SPRY2) is a modulator of tyrosine kinase receptor signaling with receptor- and cell type-dependent inhibitory or enhancing effects. Studies on the action of SPRY2 in major cancers are conflicting and its role remains unclear. Here we have dissected SPRY2 action in human colon cancer. Global transcriptomic analyses show that SPRY2 downregulates genes encoding tight junction proteins such as claudin-7 and occludin and other cell-to-cell and cell-to-matrix adhesion molecules in human SW480- ADH colon carcinoma cells. Moreover, SPRY2 represses LLGLL2/HUGL2, PATJ1/INADL and ST14, main regulators of the polarized epithelial phenotype, and ESRP1, an epithelial-to-mesenchymal transition (EMT) inhibitor. A key action of SPRY2 is the upregulation of the major EMT inducer ZEB1, as these effects are reversed by ZEB1 knock-down by means of RNA interference. Consistently, we found an inverse correlation between the expression level of claudin-7 and those of SPRY2 and ZEB1 in human colon tumors. Mechanistically, ZEB1 upregulation by SPRY2 results from the combined induction of ETS1 transcription factor and the repression of microRNAs (miR-200 family, miR-150) that target ZEB1 RNA. Moreover, SPRY2 increased AKT activation by epidermal growth factor (EGF) whereas AKT and also Src inhibition reduced the induction of ZEB1. Altogether, these data suggest that AKT and Src are implicated in SPRY2 action. Collectively, these results show a tumorigenic role of SPRY2 in colon cancer that is based on the dysregulation of tight junction and epithelial polarity master genes via upregulation of ZEB1. The dissection of the mechanism of action of SPRY2 in colon cancer cells is important to understand the upregulation of this gene in a subset of patients with this neoplasia that have poor prognosis.This study was supported by the Ministerio de Economía y Competitividad of Spain and Fondo Europeo de Desarrollo Regional (FEDER) (grant SAF2013-43468-R to A.M., SAF2011-29530 to F.X.R.); FEDERInstituto de Salud Carlos III (RD12/0036/0021 to A.M. and J.M.R., RD12/0036/0034 to F.X.R., RD12/0036/0016 to M.S., RD12/0036/0012 to H.G.P., RD06/0020/0003, PS09/00562 and PI13/00703 to J.M.R.); Comunidad de Madrid (S2010/BMD-2344 Colomics2 to A.M.); Fundación Científica de la Asociación Española contra el Cáncer (to J.M.R.); U.S. Department of Defense (CA093471 and CA110602 to E.H.); National Institutes of Health/National Cancer Institute (1R01CA155234-01 to E.H.); National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases (1R21AR062239-01 to E.H.); and the Melanoma Research Alliance (to E. H.)

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Telomerase reverse transcriptase promoter mutations in bladder cancer: High frequency across stages, detection in urine, and lack of association with outcome

Background Hotspot mutations in the promoter of the gene coding for telomerase reverse transcriptase (TERT) have been described and proposed to activate gene expression. Objectives To investigate TERT mutation frequency, spectrum, association with expression and clinical outcome, and potential for detection of recurrences in urine in patients with urothelial bladder cancer (UBC). D

A methodology for detecting the orthology signal in a PPI network at a functional complex level

Contains fulltext : 93730.pdf (preprint version ) (Open Access)13 p

A four-surface schematic eye of macaque monkey obtained by an optical method

AbstractSchematic eyes for four Macaca fascicularis monkeys were constructed from measurements of the positions and curvatures of the anterior and posterior surfaces of the cornea and lens. All of these measurements were obtained from Scheimpflug photography through the use of a ray-tracing analysis. Some of these measurements were also checked (and confirmed) by keratometry and ultrasound. Gaussian lens equations were applied to the measured dimensions of each individual eye in order to construct schematic eyes. The mean total power predicted by the schematic eyes agreed closely with independent measurements based on retinoscopy and ultrasound results, 74.2 ± 1.3 (SEM) vs 74.7 ± 0.3 (SEM) diopters. The predicted magnification of 202 μm/deg in one eye was confirmed by direct measurement of 205 μm/deg for a foveal laser lesion. The mean foveal retinal magnification calculated for our eight schematic eyes was 211 ± (SEM) μm/deg, slightly less than the value obtained by application of the method of Rolls and Cowey [Experimental Brain Research, 10, 298–310 (1970)] to our eight eyes but just 4% more than the value obtained by application of the method of Perry and Cowey [Vision Research, 12, 1795–1810 (1985)]

The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery

The International Human Epigenome Consortium (IHEC) coordinates the generation of a catalog of high-resolution reference epigenomes of major primary human cell types. The studies now presented (see the Cell Press IHEC web portal at http://www.cell.com/consortium/IHEC) highlight the coordinated achievements of IHEC teams to gather and interpret comprehensive epigenomic datasets to gain insights in the epigenetic control of cell states relevant for human health and disease

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types

Abstract Background A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability. Results We apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16− monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers. Conclusions Our data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability

Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity

Background Network analysis is a powerful way of modeling chromatin interactions. Assortativity is a network property used in social sciences to identify factors affecting how people establish social ties. We propose a new approach, using chromatin assortativity, to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network and thus investigate which proteins or chromatin marks mediate genomic contacts. Results We use high-resolution promoter capture Hi-C and Hi-Cap data as well as ChIA-PET data from mouse embryonic stem cells to investigate promoter-centered chromatin interaction networks and calculate the presence of specific epigenomic features in the chromatin fragments constituting the nodes of the network. We estimate the association of these features with the topology of four chromatin interaction networks and identify features localized in connected areas of the network. Polycomb group proteins and associated histone marks are the features with the highest chromatin assortativity in promoter-centered networks. We then ask which features distinguish contacts amongst promoters from contacts between promoters and other genomic elements. We observe higher chromatin assortativity of the actively elongating form of RNA polymerase 2 (RNAPII) compared with inactive forms only in interactions between promoters and other elements. Conclusions Contacts among promoters and between promoters and other elements have different characteristic epigenomic features. We identify a possible role for the elongating form of RNAPII in mediating interactions among promoters, enhancers, and transcribed gene bodies. Our approach facilitates the study of multiple genome-wide epigenomic profiles, considering network topology and allowing the comparison of chromatin interaction networks