RNA polymerase II primes Polycomb-repressed developmental genes throughout terminal neuronal differentiation

Polycomb repression in mouse embryonic stem cells (ESCs) is tightly associated with promoter co-occupancy of RNA polymerase II (RNAPII) which is thought to prime genes for activation during early development. However, it is unknown whether RNAPII poising is a general feature of Polycomb repression, or is lost during differentiation. Here, we map the genome-wide occupancy of RNAPII and Polycomb from pluripotent ESCs to non-dividing functional dopaminergic neurons. We find that poised RNAPII complexes are ubiquitously present at Polycomb-repressed genes at all stages of neuronal differentiation. We observe both loss and acquisition of RNAPII and Polycomb at specific groups of genes reflecting their silencing or activation. Strikingly, RNAPII remains poised at transcription factor genes which are silenced in neurons through Polycomb repression, and have major roles in specifying other, non-neuronal lineages. We conclude that RNAPII poising is intrinsically associated with Polycomb repression throughout differentiation. Our work suggests that the tight interplay between RNAPII poising and Polycomb repression not only instructs promoter state transitions, but also may enable promoter plasticity in differentiated cells

Plant-RRBS, a bisulfite and next-generation sequencing-based methylome profiling method enriching for coverage of cytosine positions

Background: Cytosine methylation in plant genomes is important for the regulation of gene transcription and transposon activity. Genome-wide methylomes are studied upon mutation of the DNA methyltransferases, adaptation to environmental stresses or during development. However, from basic biology to breeding programs, there is a need to monitor multiple samples to determine transgenerational methylation inheritance or differential cytosine methylation. Methylome data obtained by sodium hydrogen sulfite (bisulfite)-conversion and next-generation sequencing (NGS) provide genome- wide information on cytosine methylation. However, a profiling method that detects cytosine methylation state dispersed over the genome would allow high-throughput analysis of multiple plant samples with distinct epigenetic signatures. We use specific restriction endonucleases to enrich for cytosine coverage in a bisulfite and NGS-based profiling method, which was compared to whole-genome bisulfite sequencing of the same plant material. Methods: We established an effective methylome profiling method in plants, termed plant-reduced representation bisulfite sequencing (plant-RRBS), using optimized double restriction endonuclease digestion, fragment end repair, adapter ligation, followed by bisulfite conversion, PCR amplification and NGS. We report a performant laboratory protocol and a straightforward bioinformatics data analysis pipeline for plant-RRBS, applicable for any reference-sequenced plant species. Results: As a proof of concept, methylome profiling was performed using an Oryza sativa ssp. indica pure breeding line and a derived epigenetically altered line (epiline). Plant-RRBS detects methylation levels at tens of millions of cytosine positions deduced from bisulfite conversion in multiple samples. To evaluate the method, the coverage of cytosine positions, the intra-line similarity and the differential cytosine methylation levels between the pure breeding line and the epiline were determined. Plant-RRBS reproducibly covers commonly up to one fourth of the cytosine positions in the rice genome when using MspI-DpnII within a group of five biological replicates of a line. The method predominantly detects cytosine methylation in putative promoter regions and not-annotated regions in rice. Conclusions: Plant-RRBS offers high-throughput and broad, genome- dispersed methylation detection by effective read number generation obtained from reproducibly covered genome fractions using optimized endonuclease combinations, facilitating comparative analyses of multi-sample studies for cytosine methylation and transgenerational stability in experimental material and plant breeding populations

Multiparameter analysis of naevi and primary melanomas identifies a subset of naevi with elevated markers of transformation

Here we have carried out a multiparameter analysis using a panel of 28 immunohistochemical markers to identify markers of transformation from benign and dysplastic naevus to primary melanoma in three separate cohorts totalling 279 lesions. We have identified a set of eight markers that distinguish naevi from melanoma. None of markers or parameters assessed differentiated benign from dysplastic naevi. Indeed, the naevi clustered tightly in terms of their immunostaining patterns whereas primary melanomas showed more diverse staining patterns. A small subset of histopathologically benign lesions had elevated levels of multiple markers associated with melanoma, suggesting that these represent naevi with an increased potential for transformation to melanoma

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning-based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and rising incidence rate in the developed world. Molecular profiling techniques have been used to study the variability between tumours as well as cancer models such as cell lines, but their translational value is incomplete with current methods. Moreover, first generation computational methods for subtype classification do not make use of multi-omics data in full scale. Drug discovery programs use cell lines as a proxy for human cancers to characterize their molecular makeup and drug response, identify relevant indications and discover biomarkers. In order to maximize the translatability and the clinical relevance of in vitro studies, selection of optimal cancer models is imperative. We present a novel subtype classification method based on deep learning and apply it to classify CRC tumors using multi-omics data, and further to measure the similarity between tumors and disease models such as cancer cell lines. Multi-omics Autoencoder Integration (maui) efficiently leverages data sets containing copy number alterations, gene expression, and point mutations, and learns clinically important patterns (latent factors) across these data types. Using these latent factors, we propose a refinement of the gold-standard CRC subtypes, and propose best-matching cell lines for the different subtypes. These findings are relevant for patient stratification and selection of cell lines for drug discovery pipelines, biomarker discovery, and target identification

Microestructura de quesos blancos turcos bajos en grasa producidos industrialmente, influencia de la homogenización de la crema

The microstructure and fat globule distribution of reduced and low fat Turkish white cheese were evaluated. Reduced and low fat cheeses were manufactured from 1.5% and 0.75% fat milk respectively which were standardized unhomogenized and homogenized cream in a dairy plant. Homogenized and non-homogenized creams and cheese whey were analyzed for fat globule distribution and cheese samples were also analyzed for microstructure characteristics. According to the results, the homogenization of cream decreased the size of fat globules; and showed that a large number of fat particles were dispersed in the in matrix and improved the lubrication of cheese microstructure. According to the micrographs for the fat, which was not removed, they exhibited a more extended matrix with a few small fat globules compared to the defatted micrographs. Homogenization of cream produces small fat globules and unclustured fat globules were found in the resulting whey. These results are important for dairy processors for using cream homogenization as a processing tool at the industrial level.Se estudia la microestructura y distribución de los glóbulos de grasa de quesos blancos turcos bajos en grasa. Quesos con reducida y baja cantidad en grasa fueron fabricados conteniendo entre el 1,5% y 0,75% de grasa de leche, respectivamente, y con cremas homogeneizadas y no homogeneizadas, en una planta de lácteos. Las cremas homogeneizadas y no homogeneizadas y el suero de los quesos se analizaron para determinar la distribución de los glóbulos de grasa y también se analizaron las características de la microestructura de muestras de queso. De acuerdo con los resultados, la homogeneización de la crema reduce el tamaño de los glóbulos de grasa, mostrando un gran número de partículas de grasa dispersa en la matriz de caseína que mejoró la lubricación de la microestructura del queso. De acuerdo con las micrografías de la grasa que no se elimina, estas exhiben una matriz más amplia en la que hay pocos glóbulos de grasa en comparación con las micrografías de las muestras desgrasadas. La homogenización de la crema produce pequeños glóbulos de grasa y el suero resultante contiene glóbulos de grasa no incrustados. Estos resultados son importantes para los procesadores de productos lácteos, y muestran la utilidad de la homogeneización de crema como una herramienta del procesamiento a nivel industrial

The RNA workbench: Best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis

Investigation of optimal parameters for finite element solution of the forward problem in magnetic field tomography based on magnetoencephalography

This paper presents an investigation of optimal parameters for finite element (FE) solution of the forward problem in magnetic field tomography (MFT) brain imaging based on magnetoencephalography (MEG). It highlights detailed analyses of the main parameters involved and evaluates their optimal values for various cases of FE model solutions (e.g., steady-state, transient, etc.). In each case, a detail study of some of the main parameters and their effects on FE solution and its accuracy are carefully tested and evaluated. These parameters include: total number and size of 3D FE elements used, number and size of elements used in surface discretisation (of both white and grey matters of the brain), number and size of elements used for approximation of current sources, number of anisotropic properties used in steady-state and transient solutions, and the time steps used in transient analyses. The optimal values of these parameters in relation to solution accuracy and mesh convergence criteria have been found and presented

3 UN Plaza

Web site: www.unicef.or