A novel approach for determining environment-specific protein costs: the case of Arabidopsis thaliana

Abstract

Motivation: Comprehensive understanding of cellular processes requires development of approaches which consider the energetic balances in the cell. The existing approaches that address this problem are based on defining energy-equivalent costs which do not include the effects of a changing environment. By incorporating these effects, one could provide a framework for integrating 'omics' data from various levels of the system in order to provide interpretations with respect to the energy state and to elicit conclusions about putative global energy-related response mechanisms in the cell.

Results: Here we define a cost measure for amino acid synthesis based on flux balance analysis of a genome-scale metabolic network, and develop methods for its integration with proteomics and metabolomics data. This is a first measure which accounts for the effect of different environmental conditions. We applied this approach to a genome-scale network of Arabidopsis thaliana and calculated the costs for all amino acids and proteins present in the network under light and dark conditions. Integration of function and process ontology terms in the analysis of protein abundances and their costs indicates that, during the night, the cell favors cheaper proteins compared with the light environment. However, this does not imply that there is squandering of resources during the day. The results from the association analysis between the costs, levels and well-defined expenses of amino acid synthesis, indicate that our approach not only captures the adjustment made at the switch of conditions, but also could explain the anticipation of resource usage via a global energy-related regulatory mechanism of amino acid and protein synthesis.

Fig. 1.

Scale-free relationship between relative abundances and costs of proteins for day and night environment. Proteins which occur at same levels with different price are binned. The x-axis shows the average protein abundance per bin, while the y-axis depicts the average cost of proteins per bin. Both day and night environments exhibit two scale-free laws: one at average protein abundances below 8 and the other above a value of 8.

Fig. 2.

Association between amino acid levels and their costs. The Kendall rank correlation coefficients between the amino acids and the day costs (orange), night cost (black), CW cost from Craig and Weber (1998) (blue), AG cost from Akashi and Gojobori (2002) and the cost from Seligmann (2003) are calculated for a period of 24 h. The night environment is depicted in gray. Correlations which are not significant (P ≥ 0.05) are shown with filled points.

Fig. 3.

Expenses for amino acid synthesis calculated in ATP times relative concentration. The expenses for our day cost (orange) and night cost (black) as well as the costs from Craig and Weber (1998) (blue) and Seligmann (2003) (red) are calculated for a period of 24 h. The night environment is depicted in gray. The expense for Seligmann's cost is not shown due to difference in units.