Bayerlová, Michaela

[["dc.bibliographiccitation.firstpage","15482"],["dc.bibliographiccitation.issue","17"],["dc.bibliographiccitation.journal","Oncotarget"],["dc.bibliographiccitation.lastpage","15493"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Rietkötter, Eva"],["dc.contributor.author","Bleckmann, Annalen"],["dc.contributor.author","Bayerlová, Michaela"],["dc.contributor.author","Menck, Kerstin"],["dc.contributor.author","Chuang, Han-Ning"],["dc.contributor.author","Wenske, Britta"],["dc.contributor.author","Schwartz, Hila"],["dc.contributor.author","Erez, Neta"],["dc.contributor.author","Binder, Claudia"],["dc.contributor.author","Hanisch, Uwe-Karsten"],["dc.contributor.author","Pukrop, Tobias"],["dc.date.accessioned","2019-07-09T11:42:37Z"],["dc.date.available","2019-07-09T11:42:37Z"],["dc.date.issued","2015-06-20"],["dc.description.abstract","The mononuclear phagocytic system is categorized in three major groups: monocyte-derived cells (MCs), dendritic cells and resident macrophages. During breast cancer progression the colony stimulating factor 1 (CSF-1) can reprogram MCs into tumor-promoting macrophages in the primary tumor. However, the effect of CSF-1 during colonization of the brain parenchyma is largely unknown. Thus, we analyzed the outcome of anti-CSF-1 treatment on the resident macrophage population of the brain, the microglia, in comparison to MCs, alone and in different in vitro co-culture models. Our results underline the addiction of MCs to CSF-1 while surprisingly, microglia were not affected. Furthermore, in contrast to the brain, the bone marrow did not express the alternative ligand, IL-34. Yet treatment with IL-34 and co-culture with carcinoma cells partially rescued the anti-CSF-1 effects on MCs. Further, MC-induced invasion was significantly reduced by anti-CSF-1 treatment while microglia-induced invasion was reduced to a lower extend. Moreover, analysis of lung and breast cancer brain metastasis revealed significant differences of CSF-1 and CSF-1R expression. Taken together, our findings demonstrate not only differences of anti-CSF-1 treatment on MCs and microglia but also in the CSF-1 receptor and ligand expression in brain and bone marrow as well as in brain metastasis."],["dc.identifier.doi","10.18632/oncotarget.3855"],["dc.identifier.fs","618466"],["dc.identifier.pmid","26098772"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13609"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58709"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1949-2553"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.subject.mesh","Animals"],["dc.subject.mesh","Antibodies, Monoclonal"],["dc.subject.mesh","Brain"],["dc.subject.mesh","Brain Neoplasms"],["dc.subject.mesh","Breast Neoplasms"],["dc.subject.mesh","Cell Line, Tumor"],["dc.subject.mesh","Cell Movement"],["dc.subject.mesh","Cell Proliferation"],["dc.subject.mesh","Female"],["dc.subject.mesh","Humans"],["dc.subject.mesh","Interleukin-1"],["dc.subject.mesh","MCF-7 Cells"],["dc.subject.mesh","Macrophage Colony-Stimulating Factor"],["dc.subject.mesh","Macrophages"],["dc.subject.mesh","Mice"],["dc.subject.mesh","Mice, Inbred BALB C"],["dc.subject.mesh","Microglia"],["dc.subject.mesh","Monocytes"],["dc.subject.mesh","Neoplasm Invasiveness"],["dc.subject.mesh","Receptor, Macrophage Colony-Stimulating Factor"],["dc.title","Anti-CSF-1 treatment is effective to prevent carcinoma invasion induced by monocyte-derived cells but scarcely by microglia."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0197162"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Wolff, Alexander"],["dc.contributor.author","Bayerlová, Michaela"],["dc.contributor.author","Gaedcke, Jochen"],["dc.contributor.author","Kube, Dieter"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2019-07-09T11:45:42Z"],["dc.date.available","2019-07-09T11:45:42Z"],["dc.date.issued","2018"],["dc.description.abstract","BACKGROUND: Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand. Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances. METHODS: Four commonly used RNA-Seq pipelines (STAR/HTSeq-Count/edgeR, STAR/RSEM/edgeR, Sailfish/edgeR, TopHat2/Cufflinks/CuffDiff)) were investigated on multiple levels (alignment and counting) and cross-compared with the microarray counterpart on the level of gene expression and gene ontology enrichment. For these comparisons we generated two matched microarray and RNA-Seq datasets: Burkitt Lymphoma cell line data and rectal cancer patient data. RESULTS: The overall mapping rate of STAR was 98.98% for the cell line dataset and 98.49% for the patient dataset. Tophat's overall mapping rate was 97.02% and 96.73%, respectively, while Sailfish had only an overall mapping rate of 84.81% and 54.44%. The correlation of gene expression in microarray and RNA-Seq data was moderately worse for the patient dataset (ρ = 0.67-0.69) than for the cell line dataset (ρ = 0.87-0.88). An exception were the correlation results of Cufflinks, which were substantially lower (ρ = 0.21-0.29 and 0.34-0.53). For both datasets we identified very low numbers of differentially expressed genes using the microarray platform. For RNA-Seq we checked the agreement of differentially expressed genes identified in the different pipelines and of GO-term enrichment results. CONCLUSION: In conclusion the combination of STAR aligner with HTSeq-Count followed by STAR aligner with RSEM and Sailfish generated differentially expressed genes best suited for the dataset at hand and in agreement with most of the other transcriptomics pipelines."],["dc.identifier.doi","10.1371/journal.pone.0197162"],["dc.identifier.pmid","29768462"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15290"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59290"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.haserratum","/handle/2/63504"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.subject.mesh","Burkitt Lymphoma"],["dc.subject.mesh","Cell Line, Tumor"],["dc.subject.mesh","Gene Expression Regulation, Neoplastic"],["dc.subject.mesh","High-Throughput Nucleotide Sequencing"],["dc.subject.mesh","Humans"],["dc.subject.mesh","Oligonucleotide Array Sequence Analysis"],["dc.subject.mesh","RNA, Neoplasm"],["dc.subject.mesh","Rectal Neoplasms"],["dc.title","A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]