Studies

Scope: The COVID-19 disease can have gastrointestinal manifestation. The virus replicates in the gut and has potential faecal-oral transmission besides airborne transmission (Lamers et al., 2020). Intestinal organoids are a proven experimental model of the human gut and can help understand the viral infection of the gut without animal models and additional biopsies. Single-cell RNA-seq techniques can distinguish the SARS-CoV-2 replicating cells and thus help to understand how cells respond to the

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An exploration on gene expression data was carried out on single-cell RNAseq analyses of bronchoalveolar lavages from nine COVID-19 patients, three moderate cases, one severe case and five critical cases (GSE145826) (doi: 10.1038/s41591-020-0901-9). To these data, single-cell RNA-sequencing from one COVID-19 lung biopsy, ~10 weeks after initial infection was added to represent persistent severe COVID19 patient group (3 weeks after symptom onset) (GSE163919). For this analysis, the epithelial cell

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Measure Gre2p activity by following the change in NADPH absorbance at 340 nm for the conversion of different substrates.

Measure binding of reactants to Gre2p by ITC (isothermal titration calorimetry).

Measure kinetics of reactants by Gre2p with ITC (isothermal titration calorimetry).

Measure homogeneity of an enzyme sample (Gre2p) with DLS (dynamic light scattering).

To allow detailed visual analysis of the overall system and its parts, we used a customised version of our Vanted extension LMME (Large Metabolic Model Explorer) to construct an overview graph, showing one node per pathway and the respective interconnecting species.

We performed a comprehensive analysis of node centralities on two levels: on the level of the individual pathway networks as well as on the level of an aggregated network which is composed of the individual networks. This allows

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Common standard quality control parameters involve the number of genes and transcripts per cell and the fraction of transcripts from mitochondrial genes (%mtDNA). While cutoffs for transcripts and genes per cell are usually user-defined for each experiment or individually calculated, a fixed threshold of 5% mtDNA is standard and set as default in scRNA-Seq software. However, in heart, mitochondrial transcripts comprise almost 30% of total mRNA. In this study, we demonstrate that a 5%-threshold

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This study contains our snRNA-Seq based comparison of whole hearts from Fzt.DU and Bl6 mice published in Cardiovascular Research.

We further used the transcriptome dataset from the GEO database with accession number GSE147507 (Blanco-Melo et al., 2020) to extract the series number 5 from the dataset, consisting of 2 conditions in triplicate, A549 cells treated with a mock and A549 infected with SARS-CoV-2, measured 24 hours after treatment. Phosphoproteomic data of mock-treated and SARS-CoV2 infected cells were extracted from (Stukalov et al., 2020). We then applied our pipeline described in M&M X. This work notably

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In this study, we developed a workflow to compute a modified version of the Cumulative Allele Probability (CAP) for genes in the COVID-19 disease map and the “Drug Risk Probability” (DRP) score for drugs targeting genes in the map (Schärfe et al., 2017). The CAP score considers the number of pharmacogenomic variants and their frequency in the population for a specific gene. The DRP score combines the CAP scores for all drug target genes for a specific drug. For this, we use allelic frequencies

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In this study, we developed an automated and reproducible workflow for transcriptomics data analysis using network biology approaches. The analyses are fully automated in R with clusterProfiler and RCy3 to connect to the widely adopted network analysis software Cytoscape including the CyTargetLinker app for network extension. For demonstration, we use a publicly available dataset from Blanco-Melo et al., GSE147507 obtained from GEO. After pre-processing with DESeq2, the dataset contains log2 fold

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Jennifer E. Kay, Joshua J. Corrigan, Amanda L. Armijo, Ilana S. Nazari, Ishwar N. Kohale, Dorothea K. Torous, Svetlana L. Avlasevich, Robert G. Croy, Dushan N. Wadduwage, Sebastian E. Carrasco, Stephen D. Dertinger, Forest M. White, John M. Essigmann, Leona D. Samson, Bevin P. Engelward

doi: https://doi.org/10.1101/2021.01.12.426356

https://www.cell.com/cell-reports/fulltext/S2211-1247(21)00178-9?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2211124721001789%3Fshowall%3Dtrue

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Chemical structures, physicochemical properties and biological results for the compounds of the Ty-Box library

The P2011 model was rescaled to match TiMet RNA data in clock mutants from Flis et al. 2015, attached here

In this study, we integrate COVID19 Disease Maps curated regulatory information in a macrophage logical model.

This allows logical simulations of the effects of acute inflammation caused by the SARS-CoV-2 virus, both in general and in a cell-specific perspective. Moreover, understanding the regulatory network behavior of macrophages following infection opens new ways to test and predict drug and drug combination effects, as a first step towards the development of new treatments.

The hallmarks mapping schemes under comparison were developed over the period of 7 years and therefore were developed using different versions of the Gene Ontology and associated annotation. Understanding which differences between mapping schemes were the result of topological or annotation changes to GO could therefore help to further refine consensus and make results and conclusions more comparable between studies.

Multiple studies have devised mapping schemes to associate cancer hallmarks with Gene Ontology terms and biological pathway. This study compares the similarities and differences between them, in order to establish consensus knowledge.

This study examines how different hallmark gene datasets intersect with prognostic cancer genes

Interspecies differences in sensitivity to chemical exposures pose a great challenge in toxicological risk assessments. How an organism copes with chemicals is largely determined by the genes and proteins that collectively function to defend against, detoxify and eliminate chemical stressors. This integrative network includes receptors and transcription factors, biotransformation enzymes, transporters, antioxidants, and metal- and heat-responsive genes, and is collectively known as the chemical

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To achieve data “FAIRification by standardisation” and enable the user community to integrate heterogeneous and complex data, recommendations and guidelines will be developed for the consistent use of domain-specific standards for data formats, as well as for consistent data descriptions based on established metadata standards and terminologies. This standardisation concept will be based on existing standards, such as ISO 20691 and will include the definition of a minimal metadata set for

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This task T2.3 will target data quality as the “degree to which a set of inherent characteristics of data fulfils requirements” and provide consented standards and metrics to assess the data quality at different stages of the scientific data lifecycle.

(1) It will first consider FAIR standards in collaboration with the FAIRMetrics group, FAIRsharing, and RDA FAIR Data Maturity Model group.

(2) The second focus will be on adherence to defined data and metadata standards as recommended by nfdi4health

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T2.4 will define standardisation requirements and develop guidelines, as well as standard-based solutions for data access and interoperability in the defined use cases. To ensure compatibility with existing efforts aiming to improve data interoperability in medicine and healthcare, T2.4 also will coordinate its work closely with the same standardisation initiatives and technical committees of standardisation organisations as T2.2. To enable a seamless access and exchange of health data within the

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This task aims for policies for data management and publication in order to make data of public health studies findable and interoperable. To find information about studies and (meta-)data and to ensure their interoperability, it is necessary to document the descriptive core elements in a structured way already when planning projects. This applies both to data management and to the subsequent publication of research results and data and is particularly important for research projects handling

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Motivated by an increasing population and the desire to grow plants more efficiently,

attention has turned to the use of Light Emitting Diodes (LEDs) to illuminate plants

which are grown indoors. Indoor growing facilities enable closely controlled and mon-

itored environmental conditions. More and more of these facilities exchange High

Pressure Sodium (HPS) lamps for LED lighting since they provide more efficient

lighting and the possibility to control light intensity and quality in order to

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This study includes the single snRNA-seq in whole adult murine hearts from an inbred (C57BL/6NRj) and an outbred (Fzt:DU) mouse strain in comparison to publicly available scRNA-seq data of the tabula muris project.