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Maren Depke Discussion and Conclusions Only few genomewide transcriptome profiling studies on IFN-γ and macrophages are available in literature. Kota et al. analyzed the reaction of murine RAW 264.7 cells to IFN-γ exposition for 4 h (Kota et al. 2006). Despite the difference of macrophage cell line to BMM and of treatment time and IFN-γ dose (4 h and 1000 U/ml vs. 24 h and 300 U/ml) between the published study and the study described in this thesis, a very good agreement in the differential regulation of several genes was observed (e. g. Oas, GTPases, Socs1, Socs3, Nos2, Cxcl9, Cxcl10, Irf1, Cxcr4, immunoproteasome and associated genes, MHC molecules, Il18bp, Il10ra, Ctsc, Ptgs2 and others). Ehrt et al. analyzed BMM after 72 h of IFN-γ treatment (100 U/ml), infection for 24 h with Mycobacterium tuberculosis, or both (Ehrt et al. 2001). The authors observed – in contrast to the results of this study – slightly more repressed than induced genes after IFN-γ treatment alone. The infection setting resulted in an additional, specific set of differentially expressed genes. Such additional set of genes is also expected in case of future inclusion of further infected samples in the settings of the study described in this thesis. The authors defined a set of approximately 1300 genes to be regulated upon treatment of BMM with IFN-γ (Ehrt et al. 2001). This high number is not directly comparable with the results of this study, because Ehrt and colleagues treated each probe set of the array as if it represented a single gene. This simplification was necessary because the analysis was performed before the complete sequencing of the mouse genome was finished. Thus, at that time, the partially overlapping EST and cDNA sequences were difficult to assign to gene information. Furthermore, the authors explain the result of the high number of regulated genes with the long IFN-γ stimulation time of 72 h. But also between the study by Ehrt et al. and the study described in this thesis accordance was observed (e. g. MHC molecules, Gbp2 and other GTPases, Irg1, Nos2, Cxcl10, Cxcr4). Interestingly, the authors monitored a strong influence of Nos2 deficiency on the gene expression profile after IFN-γ treatment leading to the conclusion that Nos2 directly or indirectly influences the cellular reaction to IFN-γ stimulus. Zocco and coworkers focused their analyses on rat hepatic macrophages, i. e. Kupffer cells, stimulated with 1000 U/ml IFN-α or IFN-γ for 8 h (Zocco et al. 2006). Using an Affymetrix array with about 8800 probe sets, they observed 70 induced and 72 repressed genes by IFN-γ, the relation of which resembles more that observed by Ehrt et al. than the relation determined in the study described in this thesis. Nevertheless, also here a certain concordance of cellular reaction became visible (e. g. Mx, Gbp2, Nos2, Irf1, Stat1, Oas, immunoproteasome subunits, MHC molecules). In the study of Pereira et al. BMM of A/J or BALB/c mice were treated for 18 h with 50 U/ml of IFN-γ and analyzed on a 1536 feature cDNA array from a fetal thymus library (Pereira et al. 2004). For BALB/c BMM, the induction of 297 genes and repression of 58 genes was recorded. Here, the relation of induction to repression is similar to that observed in the study described in this thesis although the comparison of results is difficult because of the very different arrays, which were used in both studies. The comparison with similar transcriptome studies from literature leads to the conclusion that the results of this study are well supported by knowledge on cellular reactions to IFN-γ. In addition, similar to comparisons of other studies, also in this study specific gene expression changes were observed which might be explained with experimental differences. Nevertheless, for a comprehensive knowledge of IFN-γ effects the study of different experimental settings is necessary to which this study contributes. During data analysis using the Ingenuity Pathway Analysis tool (IPA, www.ingenuity.com), it became clear that only a fraction of the IFN-γ related, differentially expressed genes were linked to macrophages or RAW cells in the IPA database. Of about 180 genes, approximately 130 were 188
Maren Depke Discussion and Conclusions associated with IFN-γ in other cells, but not in macrophages/RAW cells. Thus, this study provides a significant contribution to extend the scientific knowledge of IFN-γ effects in macrophages. Strain differences in gene expression occurred between BALB/c BMM and C57BL/6 BMM on both treatment levels. Most differences were similar at control level and after IFN-γ treatment, and the differences included genes, which were expressed at a higher level in BALB/c BMM or in C57BL/6 BMM. Some insights into the data indicate that the differences might be immunerelevant. For example Cxcl11, the Th1 cell attracting chemokine, differed between the strains after IFN-γ treatment because it was induced from a common baseline level only in BALB/c BMM. Since it is known that the immune response of C57BL/6 mice is balanced towards Th1 with high IFN-γ/low IL-4 production in splenocytes after concanavalin A stimulation and that of BALB/c mice towards Th2 with low IFN-γ/high IL-4 production in splenocytes after concanavalin A stimulation (Mills et al. 2000), the difference after IFN-γ treatment was unexpected. But as the IFN-γ stimulus was added externally, the difference might display a compensatory variation of BALB/c BMM gene expression. C57BL/6 macrophages were reported to produce more NO than BALB/c macrophages (Mills et al. 2000). The induction of Nos2 by IFN-γ in C57BL/6 BMM was by a factor of about 1.4 stronger than in BALB/c BMM in this study, but the difference was not significant although a trend for confirmation of literature data was visible. Furthermore, BALB/c macrophages were observed to produce more ornithine/urea because of a stronger arginase activity (Mills et al. 2000). In this study, BALB/c BMM exhibited higher expression of arginase 2 than C57BL/6 at control and at IFN-γ treated level as indicated by literature data. The phenotypical differences between the reaction of BALB/c and C57BL/6 BMM were often determined in the presence of IFN-γ and a second stimulus like LPS or infection (Breitbach et al. 2006, Eske et al. 2009). To elucidate molecular reasons for the observed differences in killing of pathogens or cytokine production, the inclusion of samples subjected to a second stimulus in addition to IFN-γ is recommended. The experimental setting of this study was planned with the aim to analyze basic principles of murine BMM: the reaction to the priming signal IFN-γ on a molecular level and differences of reaction between the BMM of both mouse strains. To further elucidate reasons for the different success of the mouse strains to fight and overcome infections in vivo and in vitro, experiments will need to be expanded to the analysis of IFN-γ treatment in combination with bacterial infection. This might include studies using Burkholderia infections for which attenuated mutants exist of whose study interesting results are anticipated. Furthermore, the first proteome analyses were performed for infection experiments of BMM with S. aureus, of which the first results are presented in the thesis of Dinh Hoang Dang Khoa. Further complementing results are expected from the extension of the analysis to transcriptome level. 189
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G E N O M E W I D E C H A R A C T E
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Maren Depke C O N T E N T S GENOMEW
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Maren Depke Z U S A M M E N F A S S
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Maren Depke Zusammenfassung der Dis
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Maren Depke S U M M A R Y O F D I S
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Maren Depke Summary of Dissertation
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Maren Depke I N T R O D U C T I O N
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Maren Depke Introduction Besides op
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Maren Depke Introduction Extracellu
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Maren Depke Introduction with heigh
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Maren Depke Introduction 2005). SaP
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Maren Depke Introduction contains a
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Maren Depke Introduction via fibron
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Maren Depke Introduction cathelicid
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Maren Depke Introduction shock are
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Maren Depke Introduction STUDIES OF
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Maren Depke Introduction are effect
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Maren Depke Introduction cell stimu
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Maren Depke Introduction channel CF
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Maren Depke M A T E R I A L A N D M
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Maren Depke Material and Methods Li
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Maren Depke Material and Methods Ki
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Maren Depke Material and Methods GE
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Maren Depke Material and Methods Ge
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Maren Depke Material and Methods Ho
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Maren Depke Material and Methods Ho
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Maren Depke Material and Methods Pa
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corticosterone [pg/ml plasma] corti
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Maren Depke Results Liver Gene Expr
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lood glucose levels [nmol/l] resist
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LBP [ng/ml] CPR [ng/ml] Maren Depke
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Maren Depke Results Liver Gene Expr
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liver weight [g] Maren Depke Result
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infection rate cfu / 10 mg tissue c
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Maren Depke Results Kidney Gene Exp
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Table R.2.1: Genes displaying stati
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Maren Depke BR1 sign DsigB vs wt BR
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Maren Depke Results GENE EXPRESSION
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Maren Depke Results Gene Expression
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log2(ratio C57BL/6 / BALB/c) at IFN
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Maren Depke Results Host Cell Gene
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Maren Depke Results Pathogen Gene E
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Page 208 and 209: Maren Depke References Athanasopoul
Page 210 and 211: Maren Depke References Byrne GI, Le
Page 212 and 213: Maren Depke References Demling R. T
Page 214 and 215: Maren Depke References Foss GS, Pry
Page 216 and 217: Maren Depke References Hagopian K,
Page 218 and 219: Maren Depke References Jin T, Bokar
Page 220 and 221: Maren Depke References Kwan T, Liu
Page 222 and 223: Maren Depke References Luster AD, U
Page 224 and 225: Maren Depke References Namiki S, Na
Page 226 and 227: Maren Depke References Puccetti P.
Page 228 and 229: Maren Depke References Siewert E, B
Page 230 and 231: Maren Depke References van den Akke
Page 232 and 233: Maren Depke References Yang XL, Sch
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Maren Depke Acknowledgments Kidney
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