Two recent computational studies show that expression relationships between genes change with age – for example, some genes have expression levels that are highly correlated in early adulthood but not in old age. Both studies propose new methods for identifying gene groups with this behaviour, and the second also makes a compelling case that many related genes lose coexpression with age. Crucially, the correlation between a pair of genes may change with age even when the average expression levels of both genes do not – so these new coexpression methods are complementary to traditional differential expression analyses of microarray data.

Gillis et al. developed a new framework for identifying pairs of genes differentially coexpressed with age that is based on Haar wavelets, and tested it on a large set of human expression data mined from the handy GEMMA database. Unlike other methods that can interpret data coming from only two groups (e.g. young mice vs. old), the new wavelet method is designed to handle multiple ordered groups – such as animals of many different ages. The authors don’t discuss the biological implications of their results in any detail, instead promising these will be explored in a later paper.

Southworth et al. showed that coexpression patterns of groups of related genes become less coherent as animals age. Using several different methods for grouping genes together (e.g. assigning genes to the same group if they share a function, or if they are targets of the same transcription factor), they calculated intra-group correlation in 16- and 24-month-old mice using data from the AGEMAP study. They identified a surprisingly large number of groups with lower correlation in old mice. One of these is the targets of NF-κB – a transcription factor that, when knocked down, can reverse skin aging. Only a few groups (including one enriched for DNA damage genes) showed higher correlation in old mice. Also, the authors found that genes showing decreases in correlation aren’t randomly located on the chromosome – instead, they form several clusters.

What are the causes and consequences of these changes in gene group correlation? Previous single-cell studies have shown that transcriptional noise, or cell-to-cell variation in the expression levels of individual genes, increases with age. Clearly transcriptional noise is going to affect coexpression to some degree: any increase in a gene’s noise level will automatically reduce its calculated coexpression with other genes. But changes in coexpression can also occur without any corresponding change in noise. These changes may reflect cellular processes that are active or suppressed at different times of life, and many or all such changes (such as a ramped-up DNA damage response in old age) may be adaptive. Further analyses are needed to tease out which age-related coexpression differences result from noise, and which ones are telling us something new.

Gillis, J., & Pavlidis, P. (2009). A methodology for the analysis of differential coexpression across the human lifespan BMC Bioinformatics, 10 (1) DOI: 10.1186/1471-2105-10-306

Southworth, L., Owen, A., & Kim, S. (2009). Aging Mice Show a Decreasing Correlation of Gene Expression within Genetic Modules PLoS Genetics, 5 (12) DOI: 10.1371/journal.pgen.1000776