Most microarray studies of aging animals try to associate gene expression with chronological age: they look for groups of genes that are upregulated or downregulated as we get older. But chronological age is often an imperfect proxy for the quantity we are really interested in – physiological age, or bodily health, which is notoriously difficult to quantify.
The search is on for informative correlates of physiological age (like telomere length) that can be used to assess current health and predict remaining lifespan. One seemingly relevant quantity that is simple to measure is liveliness: we might expect a sprightly 90 year old woman to look forward to more years of healthy life than a lethargic 80 year old.
In the latest issue of Aging Cell, Golden et al. argue that this kind of behavior is a useful proxy for physiological age, and then show that gene expression can be used to predict behavior:
Age-related behaviors have distinct transcriptional profiles in C.elegans
There has been a great deal of interest in identifying potential biomarkers of aging (Butler et al. 2004). Biomarkers of aging would be useful to predict potential vulnerabilities in an individual that may arise well before they are chronologically expected, due to idiosyncratic aging rates that occur between individuals. Prior attempts to identify biomarkers of aging have often relied on the comparisons of long-lived animals to a wild-type control (Dhahbi et al. 2004). However, the effect of interventions in model systems that prolong lifespan (such as single gene mutations, or caloric restriction) can sometimes be difficult to interpret due to the manipulation itself having multiple unforeseen consequences on physiology, unrelated to aging itself (Gems et al. 2002; Partridge and Gems 2006). The search for predictive biomarkers of aging therefore is problematic, and the identification of metrics that can be used to predict either physiological or chronological age would be of great value (Butler et al. 2004). One methodology which has been used to identify biomarkers for numerous pathologies is gene expression profiling. Here, we report whole-genome expression profiles of individual wild-type Caenorhabditis elegans covering the entire wild-type nematode life span. Individual nematodes were scored for either age-related behavioral phenotypes, or survival, and then subsequently associated with their respective gene expression profiles. This facilitated the identification of transcriptional profiles that were highly associated with either physiological or chronological age. Overall, our approach serves as a paradigm for identifying potential biomarkers of aging in higher organisms that can be repeatedly sampled throughout their lifespan.
In the study, worms are grouped into 3 broad categories based on their behavior: category A if they show “symmetric, spontaneous, and smooth movement, ” C if they “only move their nose or tail when prodded,” and B for anything in between. Using data from a previous work, Golden et al. show that behavior is a useful proxy for physiological age: C worms have a significantly shorter remaining lifespan than A worms (after controlling for chronological age). In other words, in our search for biomarkers of physiological aging, we should include genes whose expression levels predict behavior – not just those that predict chronological age.
Golden et al. then get on to their main experiment: they measure mRNA expression levels in worms of 7 different ages, and grade the behavior of each worm. Using a machine learning approach, they show that 71% of the time, gene expression levels accurately predict behavior class. Several of the genes that are more highly expressed in inert C worms (versus in active A worms) are involved in amino acid metabolism and/or related to actin – the authors speculate that physiologically old worms might upregulate these genes to try to repair their damaged cytoskeletons.
Their full results are available online in the GEO database (GSE12290) – this should prove a wonderful resource for bioinformaticians, especially as it is only the second full-genome microarray study of aging in the worm.