Abstract

The current increase in life expectancy observed in industrialized societies underscores the need to achieve a better understanding of the aging process that could help the development of effective strategies to achieve healthy aging. This will require not only identifying genes involved in the aging process, but also understanding how their effects are modulated by environmental factors, such as dietary intake and life style. Although the human genome has been sequenced, it may be impractical to study humans or other long-lived organisms to gain a mechanistic understanding about the aging process. Thus, short-lived animal models are essential to identifying the mechanisms and genes that affect the rate and quality of aging as a first step towards identifying genetic variants in humans. In this study, we investigated gene expression changes between two strains of Drosophila (Oregon and 2b) for which quantitative trait loci (QTLs) affecting life span were identified previously. We collected males and females from both strains at young and old ages, and assessed whole genome variation in transcript abundance using Affymetrix GeneChips. We observed 8217 probe sets with detectable transcripts. A total of 2371 probe sets, representing 2220 genes, exhibited significant changes in transcript abundance with age; and 839 probe sets were differentially expressed between Oregon and 2b. We focused on the 359 probe sets (representing 354 genes) that exhibited significant changes in gene expression both with age and between strains. We used these genes to integrate the analysis of microarray gene expression data, bioinformatics, and the results of genetic mapping studies reported previously, to identify 49 candidate genes and four pathways that could potentially be responsible for regulating life span and involved in the process of aging in Drosophila and humans.

Keywords: Life span; Microarray gene expression; QTL; Drosophila aging

Article Outline

1. Introduction

2. Materials and methods

2.1. Drosophila stocks

2.2. Transcript profiling

2.3. Statistical analysis

2.4. Sequence analysis

2.5. Gene ontology and pathway analysis

3. Results and discussion

3.1. Patterns of differential expression

3.2. Biomarkers of aging

3.3. Genetic variation in transcript abundance

3.3.1. Biological process

3.3.2. Molecular function

3.3.3. Cellular component

3.3.4. Pathways

3.4. Integration of gene expression and QTL mapping

3.5. Age-related expression changes of homologs of human metabolic syndrome genes

3.6. Future prospects

Acknowledgements

Appendix A. Supplementary data

References