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Maren Depke Material and Methods Gene Expression Pattern of Bone-Marrow Derived Macrophages after Interferon-gamma Treatment Labeled cDNA for hybridization was prepared for each sample from 200 ng totalRNA using the GeneChip Whole Transcript (WT) Sense Target Labeling Assay and hybridized to GeneChip Mouse Gene 1.0 ST Arrays according to the manufacturer’s instructions (Affymetrix, Santa Clara, CA, USA). Arrays were washed and stained using the GeneChip Hybridization, Wash, and Stain Kit in a GeneChip Fluidics Station 450 and scanned with a GeneChip Scanner 3000 (all items from Affymetrix, Santa Clara, CA, USA). Resulting array image files (CEL-files) were imported to the Rosetta Resolver System (Rosetta Biosoftware, Seattle, WA, USA). Signal intensities were extracted using the RMA algorithm and differentially expressed probe sets were accessed with error-weighted one-way Analysis of Variance (ANOVA) including Benjamini Hochberg False Discovery Rate (FDR) multiple testing correction at the analysis levels of Intensity Profiles and sequences in log-transformed space. Values of p* 0.01 on intensity profile level in Rosetta Resolver software) were not included in statistical testing. Each run of the statistical test compared two sample groups that consisted of three biological replicates each. Four comparisons were included into statistical testing: 1) IFN-γ treated BALB/c BMM versus medium control BALB/c BMM to access IFN-γ effects in BALB/c BMM, 2) IFN-γ treated C57BL/6 BMM versus medium control C57BL/6 BMM to retrieve IFN-γ effects in C57BL/6 BMM, 3) medium control C57BL/6 BMM versus medium control BALB/c BMM to obtain strain differences at the non-activated control level, and 4) IFN-γ treated C57BL/6 BMM versus IFN-γ treated BALB/c BMM to elicit strain differences at the IFN-γ activated level. The Rosetta Resolver software allows mapping of 28944 records of sequence level information (i.e. the Affymetrix probe sets) to genes via the EntrezGene nomenclature resulting in 20074 records. Additionally, the software calculates expression data for genes from the original sequence level values. This function also combines intensities of two or more probe sets for genes that are represented by more than one probe set. The lists of statistically significant differentially expressed probe sets resulting from ANOVA were translated into lists of differentially expressed genes by using the EntrezGene level annotation included in the Rosetta Resolver software. In order to exclude biologically irrelevant small changes in expression level from the statistically significant lists resulting from ANOVA, expression data on EntrezGene level was restricted to a minimal absolute fold change of 1.5. Nevertheless, only a minority of statistically significant genes did not pass this fold change cutoff. Proteome analysis [performed by Dinh Hoang Dang Khoa] Proteome analysis was performed by Dinh Hoang Dang Khoa using both gel-based and gelfree approaches. Briefly, BMM protein extracts in urea/thiourea buffer were separated by Two Dimensional Fluorescence Difference Gel Electrophoresis (2D-DIGE), and spots on the gels were identified using MALDI-MS-MS. Tryptic peptides of the protein extracts were analyzed with LTQ- FT-ICR mass spectrometer after liquid chromatographic (LC) prefractionation. Differential analysis of label-free MS data was performed using the Rosetta Elucidator software package (Rosetta Biosoftware, Seattle, WA, USA). Comparison of transcriptome and gel-free LC-MS/MS proteome results To compare results from transcriptome and gelfree proteome analysis, it was necessary to map proteins from LC-MS/MS identification to genes available on the GeneChip Mouse Gene 1.0 ST array (Affymetrix). In order to do so, protein IPI identifiers were translated into EntrezGene IDs using the PIPE (http://pipe.systemsbiology.net/pipe) and UniProt ID (www.uniprot.org) mapping tools (Lars Brinkmann). Missing EntrezGene IDs were added manually if possible. The resulting list 48
Maren Depke Material and Methods Gene Expression Pattern of Bone-Marrow Derived Macrophages after Interferon-gamma Treatment of proteins was imported into the Rosetta Resolver software using the EntrezGene IDs and compared with genes on the Affymetrix array based on the annotation of the Rosetta Resolver software. Global functional analysis of transcriptomic [Maren Depke] and proteomic [Dinh Hoang Dang Khoa] results using Ingenuity Pathway Analysis (IPA) The lists of differentially expressed genes with an absolute fold change of at least 1.5 were directly uploaded from Rosetta Resolver System to the Ingenuity Pathway Analysis tool Version 7.5 and 8.0 (IPA, Ingenuity Systems, www.ingenuity.com). IPA assigned the EntrezGene IDs to the corresponding records of the so called Ingenuity Pathway Knowledge Base (IPKB), combined related genes to networks and conducted a statistical analysis for over-represented functions and canonical pathways in the imported lists of differentially expressed items in relation to the sequences available on the GeneChip Mouse Gene 1.0 ST Array as reference set. Generally, analysis in IPA was not restricted to species, type of relationship, type of molecule or the like. Only for building user-defined pathways, a restriction to cell type “macrophages” or “RAW cells” was used. Correspondingly, protein IPI identifiers, p-values, and fold change of regulated proteins were uploaded to IPA and also analyzed by the network, function and canonical pathway tools. In this case, the whole list of identified proteins was uploaded and afterwards restricted to a minimal absolute fold change of 1.5 and a p-value of at least 0.01. This approach allowed using all identified proteins as reference set. In case of interest, automatically generated networks from separate analyses were merged using the automatic merge-tool from IPA. User-defined networks (called pathways) centered around the starting node IFN-γ were created using the grow-tool of IPA‘s pathway function. The addition of further nodes according to information stored in the so-called Ingenuity Pathway Knowledge Base (IPKB) was restricted to a list of genes/proteins that are differentially expressed in at least 1 of 4 comparisons 1) IFN-γ effects in BALB/c BMM, 2) IFN-γ effects in C57BL/6 BMM, 3) strain difference at non-treated control level and 4) strain difference after IFN-γ activation. Networks were built either without further restrictions or with additional restriction to macrophage/RAW cells (i.e. only genes/proteins in the IPKB described to be linked to IFN-γ in macrophages or RAW cells were allowed to enter the new network). Lists of genes included in the IFN-γ centered networks were compared to identify common and unique genes between the networks without and with restriction to macrophage/RAW cells and finally exported via spreadsheet. 49
Page 1 and 2: G E N O M E W I D E C H A R A C T E
Page 3 and 4: Maren Depke C O N T E N T S GENOMEW
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Page 7 and 8: Maren Depke Zusammenfassung der Dis
Page 9 and 10: Maren Depke S U M M A R Y O F D I S
Page 11 and 12: Maren Depke Summary of Dissertation
Page 13 and 14: Maren Depke I N T R O D U C T I O N
Page 15 and 16: Maren Depke Introduction Besides op
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Page 31 and 32: Maren Depke Introduction STUDIES OF
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Page 39 and 40: Maren Depke M A T E R I A L A N D M
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Page 91 and 92: Maren Depke Results GENE EXPRESSION
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Maren Depke Results Gene Expression
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Maren Depke Results Host Cell Gene
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Maren Depke Results Pathogen Gene E
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S. aureus RN1HG sample conditions a
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mean normalized intensity mean norm
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mean normalized intensity Maren Dep
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log 2 (ratio) log 2 (ratio) log 2 (
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Maren Depke D I S C U S S I O N A N
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Maren Depke Discussion and Conclusi
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Maren Depke References Athanasopoul
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Maren Depke References Byrne GI, Le
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Maren Depke References Demling R. T
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Maren Depke References Foss GS, Pry
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Maren Depke References Hagopian K,
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Maren Depke References Jin T, Bokar
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Maren Depke References Kwan T, Liu
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Maren Depke References Luster AD, U
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Maren Depke References Namiki S, Na
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Maren Depke References Siewert E, B
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Maren Depke References Yang XL, Sch
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Maren Depke A F F I D A V I T / E R
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Maren Depke Acknowledgments Kidney
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