genomewide characterization of host-pathogen interactions by ...

More documents

Recommendations

Info

Maren Depke Discussion and Conclusions GENE EXPRESSION PATTERN OF BONE-MARROW DERIVED MACROPHAGES AFTER INTERFERON-GAMMA TREATMENT The phagocytes of the innate immune system, neutrophils, monocytes/macrophages and dendritic cells, accomplish central functions during the encounter of host and pathogen. They are the first group of immune cells which recognize and react to an infection and the main effectors of clearance of pathogens. Beside the different location in the non-reactive situation, when neutrophils and monocytes circulate in the blood stream and macrophages and dendritic cells are found in the tissue, macrophages and dendritic cells are long-living in comparison to the shortliving neutrophils. Macrophages and dendritic cells have important functions in the regulation of the immune reaction, e. g. by their antigen presentation ability (Gordon S 2007). The central position of macrophages in the immune defense accounts for the relevance of research on macrophage related topics. Accordingly, the study described in this thesis aimed to analyze on a molecular level the reaction of macrophages to interferon-γ (IFN-γ), which in low doses is known to be a priming signal for macrophages and prepares the cells for a faster reaction to a second stimulus (Dalton et al. 1993, Huang et al. 1993, Ma J et al. 2003, Mosser 2003). Here, by utilization of bonemarrow derived macrophages (BMM), which were differentiated in vitro from bone marrow stem cells under the influence of granulocyte-macrophage colony stimulating factor (GM-CSF), any influence of immunological conditioning or in vivo stimuli were avoided. Such influences could impair the experimental results when using mature macrophages prepared from animal organs. Furthermore, recently established serum-free culture conditions (Eske et al. 2009) were applied. This new system circumvented uncontrollable influences on BMM experiments, which would have been introduced by vendor- and batch-varying, cytokine-, hormone- or endotoxincontaining serum if traditional cultivation conditions had been applied. The study included BMM of the two mouse strains BALB/c and C57BL/6, which were chosen because of the differences observed between these mouse strains in in vivo and in vitro infection studies (Breitbach et al. 2006, Autenrieth et al. 1994, van Erp et al. 2006). Thus, the data set from this study was used to compare on the one hand non-stimulated control BMM of both strains, and on the other hand BMM of the two strains after IFN-γ treatment. Another feature of this study was the combined approach of transcriptome (Maren Depke) and proteome (Dinh Hoang Dang Khoa) analysis. In the comparison of transcriptome and proteome results, it was expected that the microarray as whole genome array covered a much higher number of genes than the number of proteins covered by the gel-free LC-MS/MS proteome approach, which was already chosen as best comparable method. Anyway, a defined part of the microarray results like information on genes encoding membrane or secreted proteins can practically not match proteome results for lack of accessibility. Hence, it was not surprising to find a high number of differentially expressed genes, for which protein data were not available. Vice versa, corresponding gene expression values were recorded for almost all protein data. A good agreement of the results from both analysis levels was evident for the IFN-γ effects. Depending on the selected comparison, the overlap of differentially expressed genes and 182
Maren Depke Discussion and Conclusions proteins different in abundance amounted to approximately 40 % to 60 % in reference to genes/proteins which were accessible with both methods. Most recently, a study on LPSactivated RAW 264.7 macrophages and C57BL/6 BMM was published. Applying isotope coded affinity tagging (ICAT), multidimensional liquid chromatography, and mass spectrometry, 1064 proteins were identified and quantified of which 36 differed in abundance between LPS-treated and control cells. Comparison to microarray data revealed approximately 75% concordance (Swearingen et al. 2010). Although the treatments in both studies were different, the total, regulated and overlap numbers were roughly comparable. Nevertheless, a different phenomenon was observed when analyzing differences between BMM of both strains. Expectedly, a high number of differentially expressed genes was observed, which were not accessible with the proteome approach. But also a high number of regulated proteins was recorded, for which transcriptome data were mostly available but did not show differential expression. Thus, BMM in both strains might differ in post-transcriptional, translational, and protein stability/turnover processes. Although the regulated genes/proteins were not always identical for the different comparisons, similar global results were received by both approaches. 1) IFN-γ treatment mainly led to induction of gene expression or an increase in protein abundance. 2) IFN-γ induced changes were highly similar in BALB/c BMM and C57BL/6 BMM, since many of them were observed in BMM of both strains or, when observed only in one of them, showed a highly similar trend in the other in reference to their fold change values. 3) In the comparison between BMM of the two analyzed mouse strains, about 50 % of regulated genes/proteins exhibited higher expression/abundance in BALB/c BMM whereas the other 50 % showed higher expression/abundance in C57BL/6 BMM. 4) Strain differences were highly similar in the comparison of control level BMM and in the comparison of IFN-γ treated BMM. Apart from the comparison to proteome results, the transcriptome data were analyzed for their biological content. The confirmation of known IFN-γ effects in the data set proved the relevance of the results. Very prominent, the induction of immunoproteasome and antigen presentation genes became visible. These included Psmb8, Psmb9, Psmb10, Psme1, and Psme2, genes of proteasome subunits, Tap1 and Tap2, peptide transporters from cytosol to the endoplasmatic reticulum (ER), Erap1, ER aminopeptidase, and MHC class I and class II genes, which were already described to be induced by interferon (Aki et al. 1994, Van den Eynde/Morel 2001, Brucet et al. 2004, Ma W et al. 1997, Schiffer et al. 2002, Chang et al. 2005, Jung et al. 2009, Benoist/Mathis 1990, Boehm et al. 1997, Gobin/van den Elsen 2000). IFN-γ is also in vivo responsible for the complete exchange of the constitutive to the immunoproteasome since this exchange was shown to be reduced to 50 % in the liver of IFN-γ -/- BALB/c mice after infection with lymphocytic choriomeningitis virus. The authors suppose TNF-α to be responsible for the remaining fraction of exchange (Khan S et al. 2001). Other in vivo experiments using the fungal pathogen Histoplasma capsulatum in infections of IFN-γ -/- C57BL/6 mice resulted in a complete inability to induce the immunoproteasome. In that study, the authors could not find hints that other cytokines can compensate for the loss of IFN-γ in reference to the immunoproteasome (Barton et al. 2002). The knockout of inducible proteasome β-subunit LMP-7 (Psmb8) led to a strongly increased susceptibility of mice during Toxoplasma gondii infections, which was 183
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
Page 5 and 6:
Maren Depke Z U S A M M E N F A S S
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
Page 17 and 18:
Maren Depke Introduction Extracellu
Page 19 and 20:
Maren Depke Introduction with heigh
Page 21 and 22:
Maren Depke Introduction 2005). SaP
Page 23 and 24:
Maren Depke Introduction contains a
Page 25 and 26:
Maren Depke Introduction via fibron
Page 27 and 28:
Maren Depke Introduction cathelicid
Page 29 and 30:
Maren Depke Introduction shock are
Page 31 and 32:
Maren Depke Introduction STUDIES OF
Page 33 and 34:
Maren Depke Introduction are effect
Page 35 and 36:
Maren Depke Introduction cell stimu
Page 37 and 38:
Maren Depke Introduction channel CF
Page 39 and 40:
Maren Depke M A T E R I A L A N D M
Page 41:
Maren Depke Material and Methods Li
Page 44 and 45:
Maren Depke Material and Methods Ki
Page 47 and 48:
Maren Depke Material and Methods GE
Page 49:
Maren Depke Material and Methods Ge
Page 52 and 53:
Maren Depke Material and Methods Ho
Page 54 and 55:
Maren Depke Material and Methods Ho
Page 56 and 57:
Maren Depke Material and Methods Pa
Page 58 and 59:
Page 60 and 61:
Page 63 and 64:
corticosterone [pg/ml plasma] corti
Page 65 and 66:
Maren Depke Results Liver Gene Expr
Page 67 and 68:
lood glucose levels [nmol/l] resist
Page 69 and 70:
LBP [ng/ml] CPR [ng/ml] Maren Depke
Page 71 and 72:
Maren Depke Results Liver Gene Expr
Page 73 and 74:
liver weight [g] Maren Depke Result
Page 75 and 76:
infection rate cfu / 10 mg tissue c
Page 77 and 78:
Maren Depke Results Kidney Gene Exp
Page 79 and 80:
Table R.2.1: Genes displaying stati
Page 81 and 82:
Maren Depke BR1 sign DsigB vs wt BR
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Maren Depke Results GENE EXPRESSION
Page 93 and 94:
Maren Depke Results Gene Expression
Page 95 and 96:
log2(ratio C57BL/6 / BALB/c) at IFN
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Page 109:
Page 112 and 113:
Maren Depke Results Host Cell Gene
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133: Maren Depke Results Pathogen Gene E
Page 138 and 139: S. aureus RN1HG sample conditions a
Page 148 and 149: mean normalized intensity mean norm
Page 158 and 159: mean normalized intensity Maren Dep
Page 168 and 169: log 2 (ratio) log 2 (ratio) log 2 (
Page 171 and 172: Maren Depke D I S C U S S I O N A N
Page 173 and 174: Maren Depke Discussion and Conclusi
Page 181: Maren Depke Discussion and Conclusi
Page 205: Maren Depke Discussion and Conclusi
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
Page 235:
Maren Depke A F F I D A V I T / E R
Page 238 and 239:
Maren Depke Acknowledgments Kidney
show all

genomewide characterization of host-pathogen interactions by ...

Create successful ePaper yourself

Delete template?

Save as template?