Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life

Abstract

The diversification of life involved enormous increases in size and complexity. The evolutionary transitions from prokaryotes to unicellular eukaryotes to metazoans were accompanied by major innovations in metabolic design. Here we show that the scalings of metabolic rate, population growth rate, and production efficiency with body size have changed across the evolutionary transitions. Metabolic rate scales with body mass superlinearly in prokaryotes, linearly in protists, and sublinearly in metazoans, so Kleiber's 3/4 power scaling law does not apply universally across organisms. The scaling of maximum population growth rate shifts from positive in prokaryotes to negative in protists and metazoans, and the efficiency of production declines across these groups. Major changes in metabolic processes during the early evolution of life overcame existing constraints, exploited new opportunities, and imposed new constraints.

Fig. 1.

Relationship between whole organism metabolic rate and body mass for heterotrophic prokaryotes, protists, and metazoans plotted on logarithmic axes. Fits are RMA slopes ± SE. Data for active (filled symbols, solid line) and inactive (unfilled symbols, gray line) metabolic rates are shown. Differences in slopes among all groups are significant for both physiological states (P ≤ 0.05).

Fig. 2.

(A) Scaling of r_max (unfilled symbols) and mass-specific metabolic rate (B_ms, filled symbols) with body mass for heterotrophic prokaryotes, protists, and metazoans plotted on logarithmic axes. For r_max, the RMA slopes are 0.73 for prokaryotes, −0.26 for protists, and −0.23 for metazoans. The scalings of r_max and B_ms within groups are statistically indistinguishable from parallel (Methods), consistent with the hypothesis that metabolic rate fuels biomass production. (B) Efficiency of biomass production decreases more than tenfold across the three groups, but within a group efficiency is invariant with body size. Closed symbols are for species for which both r_max and mass-specific metabolic rates were known. Open symbols are those for which r_max values were known for a species but mass-specific metabolic rates were estimated from the regressions in Fig. 1. Horizontal lines represent estimates of the efficiency of production for each group according to the parallel-line regression model for r_max and B_ms (Methods).

Fig. 3.

Schematic representation of our hypotheses to account for the scaling of metabolic rate in prokaryotes, protists, and metazoans. Scaling within each group reflects constraints on metabolic power caused by the number of respiratory complexes, but geometric constraints on exchange surfaces and transport distances ultimately limit capacity to supply substrates to these active sites. Superlinear scaling in prokaryotes (solid blue line) reflects the increase in number of genes and metabolic enzymes with increasing cell size, until a new constraint (fading blue line) resulting from cell surface area, where the enzyme complexes and proton pumps are localized, becomes limiting, imposing sublinear scaling. Protists overcome this constraint because the respiratory complexes are in the mitochondria. Larger protists can accommodate more of these organelles, resulting in linear scaling of metabolic rate with volume of mitochondria and cell mass (solid red line), until a new geometric constraint of surface exchange or transport distance limits rate of resource supply to the mitochondria, imposing sublinear metabolic scaling (fading red line). Metabolic rates of metazoans initially tend to increase linearly with number of cells and body mass, but as vascular systems evolved to distribute resources to increasingly large bodies, geometric constraints required sublinear scaling, converging to the 3/4 power scaling of Kleiber’s law (green line).

Fig. 4.

Scaling of genome size with cell size in prokaryotes. Total number of nucleotides (above) and number of different genes (below) scale with identical slopes of 0.35, consistent with our hypothesis that scaling of metabolic power in prokaryotes reflects the number of genes and the complexity of the biochemical network.