Transcriptional and signaling analysis as a means of investigating the complexity of aging processes in human and mouse.

There has been a dramatic increase in the life expectancy of men and women in the last century. Along with the increased life span the incidence of age-related diseases such as Alzheimer’s disease has also increased. This increase has led to an elevated interest to understand and investigate the mechanisms that underlie age-related diseases. Consequently, there is an increased awareness of the necessity to understand the mechanisms of aging to better cope with age-related diseases. However, the process of aging is complex and many different mechanisms are involved, including transcriptional regulation occurring with advanced age. Studies investigating age-related transcriptional changes mainly revealed that the magnitude of these changes is small and that it is difficult to find conserved regulated age-related genes between diverse species and tissues using microarray analysis. This study was aimed at elucidating mechanisms involved in human skin and general mouse aging as well as potentially conserved mechanisms involved in mammalian aging by conducting microarray analyses using RNA derived from male and female human skin biopsies and mouse brain, heart and kidney tissues and using the same array platform and standardized parameters. The data analysis was not solely based on highlighting age-related genes but also biological processes, cellular compartments and pathways. The influence of age-dependent hormonal decline on gene expression has also been tested on a different microarray platform using two different human skin cell types – sebocytes and fibroblasts. These experiments revealed that human skin aging is accompanied by a reduction in skin collagen structure. Furthermore, the potential involvement of WNT signaling in human skin aging was observed for the first time. The analysis of hormone-treated skin cells revealed significant differences between cell types and underscores the potential that age-related transcriptional changes are cell-type specific. The studies on mouse aging identified a conserved increase of immune responses on the transcriptional level. In addition, elevated levels of radical oxygen species (ROS) suggest the oxidative stress-mediated activity of NF-kB signaling. Comparisons of the human and mouse results confirm the conserved involvement of immune responses and of metabolism-related processes including for instance glutathione metabolism a regulator of endogenous ROS levels in mammalian aging. In summary, the results suggest that transcriptional changes are most probably the downstream effect of environmental and endogenous factors constantly affecting the organism during age. In addition, the finding that similar processes are age-regulated including different genes in varying tissues such as immune and metabolism-related genes, suggests that aging is not dependent on single genes but rather on a network of genes. However, it remains unclear, which mechanisms are responsible for the induction of mammalian aging.Contents ABBREVIATIONS V LIST OF FIGURES VII LIST OF TABLES IX ABSTRACT XI ABSTRACT (GERMAN) XIII 1. INTRODUCTION 1 1.1 Aging 1 1.1.1 Aging theories 1 1.1.2 Model systems in aging research 5 1.1.3 Human skin aging 9 1.2 Whole genome microarray analyses 10 1.2.1 Molecular methods employed for gene expression analyses 10 1.2.2 Advantages and disadvantages of whole genome studies 11 1.2.3 Gene expression microarrays and aging 12 1.3 Aim of this work 13 2. MATERIAL AND METHODS 14 2.1 Model systems 14 2.1.1 Cell cultures 14 2.1.2 Human skin biopsies 14 2.1.3 Mice 15 2.2 RNA analyses 15 2.2.1 Total RNA isolation 15 2.2.2 RNA and cDNA quantification 16 2.2.3 Agarose gel electrophoresis 17 2.2.4 Reverse transcription 17 2.2.5 Real-time polymerase chain reaction (Real-Time PCR) 18 2.2.6 ENSEMBL chip hybridisation 18 2.2.7 Illumina bead chip hybridisation 20 2.2.8 Pathway analysis 21 2.3 Protein analyses 21 2.3.1 Protein isolation 21 2.3.2 Protein quantification (Bradford) 22 2.3.3 SDS-PAGE gel electrophoresis 22 2.3.4 Western blotting 23 2.4 Metabolite analyses 24 2.4.1 Lipid hydroperoxide (LPO) measurement 24 2.5 Histology 24 2.5.1 Section preparation 24 2.5.2 Hematoxylin Eosin staining 25 3. RESULTS 27 3.1 Global data analysis 27 3.1.1 Global gene expression analysis 27 3.1.2 RNA quality control 29 3.1.3 Target gene lists 30 3.1.4 Pathway analyses 31 3.1.5 Array data confirmation by Real-Time PCR 31 3.2 Human skin aging 33 3.2.1 Genetic changes in age-dependent hormone-treated cell cultures 33 3.2.2 Gene expression in young and old sun-protected skin biopsies 35 3.2.3 Analogous gene expression of in vitro and in vivo skin samples 38 3.3 Mouse aging (brain, heart & kidney) 40 3.3.1 Synchronizing the sample collection 40 3.3.2 Physical and morphological differences of young and aged mice 45 3.3.3 Age-dependent gene expression in mouse brain, heart and kidney 46 3.3.4 Age-related post-transcriptional and structural changes 53 3.4 Comparison of human and mouse aging 55 3.4.1 Correlation of age-regulated target genes 55 3.4.2 Correlation of age-regulated Kegg pathways and Gene Ontologies (BP and CC) 57 3.4.3 Correlation of target genes to published data 62 4. DISCUSSION 64 4.1 Global data analysis 64 4.2 Human skin aging 67 4.3 Mouse aging 72 4.4 The basis of aging 76 5. CONCLUSION 78 REFERENCES A PUBLICATIONS L CURRICULUM VITAE M APPENDIX N I Buffers for SDS-PAGE gel electrophoresis N II Buffers for western blotting O III Target gene lists P IV David output tables