| 1. |
- Dabrowski, Michal J., et al.
(författare)
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Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival
- 2018
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- In order to find clinically useful prognostic markers for glioma patients' survival, we employed Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm on DNA methylation (HumanMethylation450 platform) and RNA-seq datasets from The Cancer Genome Atlas (TCGA) for 88 patients observed until death. The input features were ranked according to their importance in predicting patients' longer (400+ days) or shorter (<= 400 days) survival without prior classification of the patients. Interestingly, out of the 65 most important features found, 63 are methylation sites, and only two mRNAs. Moreover, 61 out of the 63 methylation sites are among those detected by the 450 k array technology, while being absent in the HumanMethylation27. The most important methylation feature (cg15072976) overlaps with the RE1 Silencing Transcription Factor (REST) binding site, and was confirmed to intersect with the REST binding motif in human U87 glioma cells. Six additional methylation sites from the top 63 overlap with REST sites. We found that the methylation status of the cg15072976 site affects transcription factor binding in U87 cells in gel shift assay. The cg15072976 methylation status discriminates <= 400 and 400+ patients in an independent dataset from TCGA and shows positive association with survival time as evidenced by Kaplan-Meier plots.
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| 2. |
- Dramiński, Michał, et al.
(författare)
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Discovering Networks of Interdependent Features in High-Dimensional Problems
- 2016
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Ingår i: Big Data Analysis. - Cham : Springer. - 9783319269894 ; , s. 285-304
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Bokkapitel (refereegranskat)abstract
- The availability of very large data sets in Life Sciences provided earlier by the technological breakthroughs such as microarrays and more recently by various forms of sequencing has created both challenges in analyzing these data as well as new opportunities. A promising, yet underdeveloped approach to Big Data, not limited to Life Sciences, is the use of feature selection and classification to discover interdependent features. Traditionally, classifiers have been developed for the best quality of supervised classification. In our experience, more often than not, rather than obtaining the best possible supervised classifier, the Life Scientist needs to know which features contribute best to classifying observations (objects, samples) into distinct classes and what the interdependencies between the features that describe the observation. Our underlying hypothesis is that the interdependent features and rule networks do not only reflect some syntactical properties of the data and classifiers but also may convey meaningful clues about true interactions in the modeled biological system. In this chapter we develop further our method of Monte Carlo Feature Selection and Interdependency Discovery (MCFS and MCFS-ID, respectively), which are particularly well suited for high-dimensional problems, i.e., those where each observation is described by very many features, often many more features than the number of observations. Such problems are abundant in Life Science applications. Specifically, we define Inter-Dependency Graphs (termed, somewhat confusingly, ID Graphs) that are directed graphs of interactions between features extracted by aggregation of information from the classification trees constructed by the MCFS algorithm. We then proceed with modeling interactions on a finer level with rule networks. We discuss some of the properties of the ID graphs and make a first attempt at validating our hypothesis on a large gene expression data set for CD4+ T-cells. The MCFS-ID and ROSETTA including the Ciruvis approach offer a new methodology for analyzing Big Data from feature selection, through identification of feature interdependencies, to classification with rules according to decision classes, to construction of rule networks. Our preliminary results confirm that MCFS-ID is applicable to the identification of interacting features that are functionally relevant while rule networks offer a complementary picture with finer resolution of the interdependencies on the level of feature-value pairs.
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| 3. |
- Kowalczyk, Dorota A., et al.
(författare)
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Local electronic structure of stable monolayers of α-MoO3−x grown on graphite substrate
- 2021
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Ingår i: 2D Materials. - : IOP Publishing. - 2053-1583. ; 8:2
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Tidskriftsartikel (refereegranskat)abstract
- We report on van der Waals epitaxy of two-dimensional (2D) molybdenum trioxide (MoO3−x) with monolayer thickness directly grown on highly oriented pyrolytic graphite by thermal evaporation under ultrahigh vacuum. The chemical composition, electronic and crystalline lattice structures of the mono-and few-layer MoO3−x sheets are analysed. Using scanning tunnelling microscopy and spectroscopy, we investigate the electronic properties of MoO3−x as a function of the number of layers and measure the apparent energy gap to be 0.4 eV for the first three layers of MoO3−x on graphite. We carried out density functional theory calculations to shed light on the mechanism underlying the observed narrow bandgap with oxygen deficiency. Moreover, the air exposure effect on monolayer MoO3−x is investigated confirming that the apparent bandgap closes, and additionally we show the reduction of the work function from 5.7 to 4.7 eV. We prove that it is possible to synthesize the 2D, non-stoichiometric, and electrically conductive MoO3−x.
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| 4. |
- Przanowski, Piotr, et al.
(författare)
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The signal transducers Stat1 and Stat3 and their novel target Jmjd3 drive the expression of inflammatory genes in microglia
- 2014
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Ingår i: Journal of Molecular Medicine. - : Springer Science and Business Media LLC. - 0946-2716 .- 1432-1440. ; 92:3, s. 239-254
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Tidskriftsartikel (refereegranskat)abstract
- Most neurological diseases are associated with chronic inflammation initiated by the activation of microglia, which produce cytotoxic and inflammatory factors. Signal transducers and activators of transcription (STATs) are potent regulators of gene expression but contribution of particular STAT to inflammatory gene expression and STAT-dependent transcriptional networks underlying brain inflammation need to be identified. In the present study, we investigated the genomic distribution of Stat binding sites and the role of Stats in the gene expression in lipopolysaccharide (LPS)-activated primary microglial cultures. Integration of chromatin immunoprecipitation-promoter microarray data and transcriptome data revealed novel Stat-target genes including Jmjd3, Ccl5, Ezr, Ifih1, Irf7, Uba7, and Pim1. While knockdown of individual Stat had little effect on the expression of tested genes, knockdown of both Stat1 and Stat3 inhibited the expression of Jmjd3 and inflammatory genes. Transcriptional regulation of Jmjd3 by Stat1 and Stat3 is a novel mechanism crucial for launching inflammatory responses in microglia. The effects of Jmjd3 on inflammatory gene expression were independent of its H3K27me3 demethylase activity. Forced expression of constitutively activated Stat1 and Stat3 induced the expression of Jmjd3, inflammation-related genes, and the production of proinflammatory cytokines as potently as lipopolysacharide. Gene set enrichment and gene function analysis revealed categories linked to the inflammatory response in LPS and Stat1C + Stat3C groups. We defined upstream pathways that activate STATs in response to LPS and demonstrated contribution of Tlr4 and Il-6 and interferon-. signaling. Our findings define novel direct transcriptional targets of Stat1 and Stat3 and highlight their contribution to inflammatory gene expression.
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| 5. |
- Stepniak, Karolina, et al.
(författare)
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Mapping chromatin accessibility and active regulatory elements reveals pathological mechanisms in human gliomas
- 2021
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Chromatin structure and accessibility, and combinatorial binding of transcription factors to regulatory elements in genomic DNA control transcription. Genetic variations in genes encoding histones, epigenetics-related enzymes or modifiers affect chromatin structure/dynamics and result in alterations in gene expression contributing to cancer development or progression. Gliomas are brain tumors frequently associated with epigenetics-related gene deregulation. We perform whole-genome mapping of chromatin accessibility, histone modifications, DNA methylation patterns and transcriptome analysis simultaneously in multiple tumor samples to unravel epigenetic dysfunctions driving gliomagenesis. Based on the results of the integrative analysis of the acquired profiles, we create an atlas of active enhancers and promoters in benign and malignant gliomas. We explore these elements and intersect with Hi-C data to uncover molecular mechanisms instructing gene expression in gliomas. Gliomas are tumors often associated with epigenetics-related gene deregulation. Here the authors reveal an atlas of active enhancers and promoters in benign and malignant gliomas by performing whole-genome mapping of chromatin accessibility, histone modifications, DNA methylation patterns and transcriptome analysis simultaneously in multiple tumor samples.
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| 10. |
- Aad, G., et al.
(författare)
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Commissioning of the ATLAS Muon Spectrometer with cosmic rays
- 2010
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Ingår i: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 70:3, s. 875-916
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Tidskriftsartikel (refereegranskat)abstract
- The ATLAS detector at the Large Hadron Collider has collected several hundred million cosmic ray events during 2008 and 2009. These data were used to commission the Muon Spectrometer and to study the performance of the trigger and tracking chambers, their alignment, the detector control system, the data acquisition and the analysis programs. We present the performance in the relevant parameters that determine the quality of the muon measurement. We discuss the single element efficiency, resolution and noise rates, the calibration method of the detector response and of the alignment system, the track reconstruction efficiency and the momentum measurement. The results show that the detector is close to the design performance and that the Muon Spectrometer is ready to detect muons produced in high energy proton-proton collisions.
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