Abstract
In this work we present a method for the differential analysis of gene co-expression networks and apply this method to look for large-scale transcriptional changes in aging. We derived synonymous gene co-expression networks from AGEMAP expression data for 16-month-old and 24-month-old mice. We identified a number of functional gene groups that change co-expression with age. Among these changing groups we found a trend towards declining correlation with age. In particular, we identified a modular (as opposed to uniform) decline in general correlation with age. We identified potential transcriptional mechanisms that may aid in modular correlation decline. We found that computationally identified targets of the NF-ΚB transcription factor decrease expression correlation with age. Finally, we found that genes that are prone to declining co-expression tend to be co-located on the chromosome. Our results conclude that there is a modular decline in co-expression with age in mice. They also indicate that factors relating to both chromosome domains and specific transcription factors may contribute to the decline.
Highlights
Since the introduction of DNA microarrays over a decade ago, it has become possible to use genome-wide approaches to explore differences between two biological conditions, such as tumor versus healthy samples, mutant versus wild-type cells or old versus young tissues
This work clearly showed that genes could be grouped into cellular pathways based on co-expression, and provided a useful approach to categorize the function of unknown genes on a global scale [2]
We used the data from AGEMAP, a large DNA microarray study of gene expression as a function of age [10]
Summary
Since the introduction of DNA microarrays over a decade ago, it has become possible to use genome-wide approaches to explore differences between two biological conditions, such as tumor versus healthy samples, mutant versus wild-type cells or old versus young tissues. In addition to individual genes, differential expression analysis can identify groups of genes or pathways that change expression levels in an experiment. This work clearly showed that genes could be grouped into cellular pathways based on co-expression, and provided a useful approach to categorize the function of unknown genes on a global scale [2]. Since this finding, gene co-expression networks have been constructed using worm, fly, mouse, and human microarray data [3,4,5,6]. The comparison of co-expression links between orthologous genes in multiple species allows one to search for relationships that are functionally conserved [6,7]
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