@article{HenkelColemanMacGregorofInneregnySchraplauetal.2018,
  author    = {Henkel, Janin and Coleman Mac Gregor of Inneregny, Charles Dominic and Schraplau, Anne and J{\"o}hrens, Korinna and Weiss, Thomas Siegfried and Jonas, Wenke and Sch{\"u}rmann, Annette and P{\"u}schel, Gerhard Paul},
  title     = {Augmented liver inflammation in a microsomal prostaglandin E synthase 1 (mPGES-1)-deficient diet-induced mouse NASH model},
  series = {Scientific Reports},
  journal   = {Scientific Reports},
  number    = {8},
  publisher = {Nature Research},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-018-34633-y},
  pages     = {1 -- 11},
  year      = {2018},
  abstract  = {In a subset of patients, non-alcoholic fatty liver disease (NAFLD) is complicated by cell death and inflammation resulting in non-alcoholic steatohepatitis (NASH), which may progress to fibrosis and subsequent organ failure. Apart from cytokines, prostaglandins, in particular prostaglandin E-2 (PGE(2)), play a pivotal role during inflammatory processes. Expression of the key enzymes of PGE(2) synthesis, cyclooxygenase 2 and microsomal PGE synthase 1 (mPGES-1), was increased in human NASH livers in comparison to controls and correlated with the NASH activity score. Both enzymes were also induced in NASH-diet-fed wild-type mice, resulting in an increase in hepatic PGE(2) concentration that was completely abrogated in mPGES-1-deficient mice. PGE(2) is known to inhibit TNF-alpha synthesis in macrophages. A strong infiltration of monocyte-derived macrophages was observed in NASH-diet-fed mice, which was accompanied with an increase in hepatic TNF-alpha expression. Due to the impaired PGE(2) production, TNF-alpha expression increased much more in livers of mPGES-1-deficient mice or in the peritoneal macrophages of these mice. The increased levels of TNF-alpha resulted in an enhanced IL-1 beta production, primarily in hepatocytes, and augmented hepatocyte apoptosis. In conclusion, attenuation of PGE(2) production by mPGES-1 ablation enhanced the TNF-alpha-triggered inflammatory response and hepatocyte apoptosis in diet-induced NASH.},
  language  = {en}
}
@article{HoangGryzikHoppeetal.2022,
  author    = {Hoang, Yen and Gryzik, Stefanie and Hoppe, Ines and Rybak, Alexander and Sch{\"a}dlich, Martin and Kadner, Isabelle and Walther, Dirk and Vera, Julio and Radbruch, Andreas and Groth, Detlef and Baumgart, Sabine and Baumgrass, Ria},
  title     = {PRI: Re-analysis of a public mass cytometry dataset reveals patterns of effective tumor treatments},
  series = {Frontiers in immunology},
  volume    = {13},
  journal   = {Frontiers in immunology},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-3224},
  doi       = {10.3389/fimmu.2022.849329},
  pages     = {9},
  year      = {2022},
  abstract  = {Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4(+)T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.},
  language  = {en}
}
@article{OlasWahl2019,
  author    = {Olas, Justyna Jadwiga and Wahl, Vanessa},
  title     = {Tissue-specific NIA1 and NIA2 expression in Arabidopsis thaliana},
  series = {Plant Signaling \& Behavior},
  volume    = {14},
  journal   = {Plant Signaling \& Behavior},
  number    = {11},
  publisher = {Taylor \& Francis Group},
  address   = {Philadelphia},
  issn      = {1559-2316},
  doi       = {10.1080/15592324.2019.1656035},
  pages     = {5},
  year      = {2019},
  abstract  = {Nitrogen (N) is an essential macronutrient for optimal plant growth and ultimately for crop productivity Nitrate serves as the main N source for most plants. Although it seems a well-established fact that nitrate concentration affects flowering, its molecular mode of action in flowering time regulation was poorly understood. We recently found how nitrate, present at the shoot apical meristem (SAM), controls flowering time In this short communication, we present data on the tissue-specific expression patterns of NITRATE REDUCTASE 1 (NIA1) and NIA2 in planta. We show that transcripts of both genes are present throughout the life cycle of Arabidopsis thaliana plants with NIA1 being predominantly active in leaves and NIA2 in meristematic tissues.},
  language  = {en}
}
@article{OlmerEngelsUsmanetal.2018,
  author    = {Olmer, Ruth and Engels, Lena and Usman, Abdulai and Menke, Sandra and Malik, Muhammad Nasir Hayat and Pessler, Frank and Goehring, Gudrun and Bornhorst, Dorothee and Bolten, Svenja and Abdelilah-Seyfried, Salim and Scheper, Thomas and Kempf, Henning and Zweigerdt, Robert and Martin, Ulrich},
  title     = {Differentiation of Human Pluripotent Stem Cells into Functional Endothelial Cells in Scalable Suspension Culture},
  series = {Stem Cell Reports},
  volume    = {10},
  journal   = {Stem Cell Reports},
  number    = {5},
  publisher = {Springer},
  address   = {New York},
  issn      = {2213-6711},
  doi       = {10.1016/j.stemcr.2018.03.017},
  pages     = {16},
  year      = {2018},
  abstract  = {Endothelial cells (ECs) are involved in a variety of cellular responses. As multifunctional components of vascular structures, endothelial (progenitor) cells have been utilized in cellular therapies and are required as an important cellular component of engineered tissue constructs and in vitro disease models. Although primary ECs from different sources are readily isolated and expanded, cell quantity and quality in terms of functionality and karyotype stability is limited. ECs derived from human induced pluripotent stem cells (hiPSCs) represent an alternative and potentially superior cell source, but traditional culture approaches and 2D differentiation protocols hardly allow for production of large cell numbers. Aiming at the production of ECs, we have developed a robust approach for efficient endothelial differentiation of hiPSCs in scalable suspension culture. The established protocol results in relevant numbers of ECs for regenerative approaches and industrial applications that show in vitro proliferation capacity and a high degree of chromosomal stability.},
  language  = {en}
}
@article{Perscheid2021,
  author    = {Perscheid, Cindy},
  title     = {Integrative biomarker detection on high-dimensional gene expression data sets},
  series = {Briefings in bioinformatics},
  volume    = {22},
  journal   = {Briefings in bioinformatics},
  number    = {3},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1467-5463},
  doi       = {10.1093/bib/bbaa151},
  pages     = {18},
  year      = {2021},
  abstract  = {Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.},
  language  = {en}
}