A network perspective on metabolism and aging

Abstract

Aging affects a myriad of genetic, biochemical, and metabolic processes, and efforts to understand the underlying molecular basis of aging are often thwarted by the complexity of the aging process. By taking a systems biology approach, network analysis is well-suited to study the decline in function with age. Network analysis has already been utilized in describing other complex processes such as development, evolution, and robustness. Networks of gene expression and protein-protein interaction have provided valuable insight into the loss of connectivity and network structure throughout lifespan. Here, we advocate the use of metabolic networks to expand the work from genomics and proteomics. As metabolism is the final fingerprint of functionality and has been implicated in multiple theories of aging, metabolomic methods combined with metabolite network analyses should pave the way to investigate how relationships of metabolites change with age and how these interactions affect phenotype and function of the aging individual. The metabolomic network approaches highlighted in this review are fundamental for an understanding of systematic declines and of failure to function with age.

Fig. 1

The central dogma of life studied with “-omics” technologies provide a global perspective on how systems begin to fail with age.

Fig. 2

Metabolic profiles integrate the effects of diet, environment, and genetics for mechanistic studies of aging.

Fig. 3

Percent of correlated metabolites as a function of extraction column and sex. We calculated correlation coefficients between all possible pairs of metabolites among individuals for both the AE column (n = 765 metabolites) and the C18 column (n = 714 metabolites), for groups of young or old marmosets separately. In each case, there was a total of n(n−1) possible pairwise correlations. We determined the percent of all correlations that was highly significant (P < 10⁻⁷). For both columns, and for both sexes, we see more significant correlations among young individuals than among old individuals. In all four comparisons, the difference between old and young groups is highly significant (P < 10⁻¹³).

Fig. 4

Frequency distribution of correlation coefficients between quantity of metabolites and age of the marmoset, using extractions from an anion exchange column (results from a C18 column were comparable). Grey bars indicate correlation coefficients that were significantly different from zero (P < 0.01). Note that for females, a disproportionate number of metabolites decline with age (t-test of average slope across all metabolites, P < 10⁻⁷), while on average, males are not significantly different from zero.