The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented global health crisis, with several countries imposing lockdowns to control the coronavirus spread. Important research efforts are focused on evaluating the association of environmental factors with the survival and spread of the virus and different works have been published, with contradictory results in some cases. Data with spatial and temporal information is a key factor to get reliable results and, although there are some data repositories for monitoring the disease both globally and locally, an application that integrates and aggregates data from meteorological and air quality variables with COVID-19 information has not been described so far to the best of our knowledge. Here, we present DatAC (Data Against COVID-19), a data fusion project with an interactive web frontend that integrates COVID-19 and environmental data in Spain. DatAC is provided with powerful data analysis and statistical capabilities that allow users to explore and analyze individual trends and associations among the provided data. Using the application, we have evaluated the impact of the Spanish lockdown on the air quality, observing that NO 2 , CO, PM 2.5 , PM 10 and SO 2 levels decreased drastically in the entire territory, while O 3 levels increased. We observed similar trends in urban and rural areas, although the impact has been more important in the former. Moreover, the application allowed us to analyze correlations among climate factors, such as ambient temperature, and the incidence of COVID-19 in Spain. Our results indicate that temperature is not the driving factor and without effective control actions, outbreaks will appear and warm weather will not substantially limit the growth of the pandemic. DatAC is available at https://covid19.genyo.es .
Background: Human decidual stromal cells (DSCs) are involved in the maintenance and development of pregnancy, in which they play a key role in the induction of immunological maternal-fetal tolerance. Precursors of DSCs (preDSCs) are located around the vessels, and based on their antigen phenotype, previous studies suggested a relationship between preDSCs and mesenchymal stromal/stem cells (MSCs). This work aimed to further elucidate the MSC characteristics of preDSCs. Methods:We established 15 human preDSC lines and 3 preDSC clones. Physiological differentiation (decidualization) of these cell lines and clones was carried out by in vitro culture with progesterone (P4) and cAMP. Decidualization was confirmed by the change in cellular morphology and prolactin (PRL) secretion, which was determined by enzyme immunoassay of the culture supernatants. We also studied MSC characteristics: (1) In mesenchymal differentiation, under appropriate culture conditions, these preDSC lines and clones differentiated into adipocytes, osteoblasts, and chondrocytes, and differentiation was confirmed by cytochemical assays and RT-PCR. (2) The expression of stem cell markers was determined by RT-PCR. (3) Cloning efficiency was evaluated by limited dilution. (4) Immunoregulatory activity in vivo was estimated in DBA/2-mated CBA/J female mice, a murine model of immune-based recurrent abortion. (5) Survival of preDSC in immunocompetent mice was analyzed by RT-PCR and flow cytometry.Results: Under the effect of P4 and cAMP, the preDSC lines and clones decidualized in vitro: the cells became rounder and secreted PRL, a marker of physiological decidualization. PreDSC lines and clones also exhibited MSC characteristics. They differentiated into adipocytes, osteoblasts, and chondrocytes, and preDSC lines expressed stem cell markers OCT-4, NANOG, and ABCG2; exhibited a cloning efficiency of 4 to 15%; significantly reduced the embryo resorption rate (P < 0.001) in the mouse model of abortion; and survived for prolonged periods in immunocompetent mice. The fact that 3 preDSC clones underwent both decidualization and mesenchymal differentiation shows that the same type of cell exhibited both DSC and MSC characteristics.(Continued on next page)
This work was supported by the Consejería de Economía, Innovación y Ciencia, Junta de Andalucía (Grant CTS-6183, Proyectos de Investigación de Excelencia 2010 to C.R.-R.) and the Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Spain (Grants PS09/00339 and PI12/01085 to E.G.O.). E.L.-D. was supported by fellowships from the Ministerio de Educación y Ciencia, Spain and the University of Granada. The authors have no conflict of interest.
Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a ∼12% diagnostic rate, with ∼8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts, can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here, we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com.
Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a ~12% diagnostic rate, with ~8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations; and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com
Recent technical advances highlight that to understand mammalian development and human disease we need to consider transcriptional and epigenetic cell-to-cell differences within cell populations. This is particularly important in key areas of biomedicine like stem cell differentiation and intratumor heterogeneity. The recently developed nucleosome occupancy and methylome (NOMe) assay facilitates the simultaneous study of DNA methylation and nucleosome positioning on the same DNA strand. NOMe-treated DNA can be sequenced by sanger (NOMe-PCR) or high throughput approaches (NOMe-seq). NOMe-PCR provides information for a single locus at the single molecule while NOMe-seq delivers genome-wide data that is usually interrogated to obtain population-averaged measures. Here, we have developed a bioinformatic tool that allow us to easily obtain locus-specific information at the single molecule using genome-wide NOMe-seq datasets obtained from bulk populations. We have used NOMePlot to study mouse embryonic stem cells and found that polycomb-repressed bivalent gene promoters coexist in two different epigenetic states, as defined by the nucleosome binding pattern detected around their transcriptional start site.
Copy number variants (CNVs) are a major cause of rare pediatric diseases with a broad spectrum of phenotypes. Genetic diagnosis based on comparative genomic hybridization tests typically identifies ~8-10% of patients as having CNVs of unknown significance, revealing the current limits of clinical interpretation. The adoption of whole-genome sequencing (WGS) as a first-line genetic test has significantly increased the load of CNVs identified in single genomes. Alongside short- and long-read sequencing technologies, a number of pathogenicity scores have been developed for filtering and prioritizing large sets of candidate CNVs in clinical settings. However, current approaches are often based, either explicitly or implicitly, on clinically annotated reference sets, which are likely to bias their predictions. In this study we developed CNVscore, a supervised-learning approach combining tree ensembles and a Bayesian classifier trained on pathogenic and non-pathogenic CNVs from reference databases. Unlike previous approaches, CNVscore couples pathogenicity estimates with uncertainty scores, making it possible to evaluate the suitability of a model for the query CNVs. Comprehensive comparative benchmark tests across independent sets and against alternative methods showed that CNVscore effectively distinguishes between pathogenic and benign CNVs. We also found that CNVs associated with CNVscores of low uncertainty were predicted with significantly higher accuracy than those of high uncertainty. However, the performance of current scoring approaches, including CNVscore, was compromised on CNV sets enriched in highly uncertain variants and presenting unconventional features, such as functionally relevant non-coding elements or the presence of disease genes irrelevant for the clinical phenotypes investigated. Finally, we used the CNVscore framework to guide CNV scoring model selection for the French National Database of Constitutional CNVs (BANCCO), which includes clinical diagnosis annotations. The CNVscore framework provides an objective strategy for leveraging the uncertainty on bioinformatic predictions to enhance the assessment of CNV pathogenicity in rare-disease cohorts. CNVscore is available as open-source software from https://github.com/RausellLab/CNVscore and is integrated into the CNVxplorer webserver http://cnvxplorer.com.
We investigate the impact of imports of intermediate inputs on export value and portfolio among Spanish regular two‐way trade of manufacturing firms over the period 1997–2018. After controlling for firm characteristics and addressing endogeneity, we find that firms that import intermediate inputs from non‐EUEFTA countries enhance the volume and scope of their EU15 exports. The magnitude of the impact is similar for high‐income countries and low‐income countries and there are no changes in sourcing patterns before or after the financial crisis.
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