Proceedings of the National Academy of SciencesEvolutionary BiologyPeer-reviewed journal article
Evolutionary bioenergetics of sporulation
Abstract
Energy is required for the expression and maintenance of complex traits. In many habitats, however, free energy available to support biosynthesis is in vanishingly short supply. As a result, many taxa have evolved persistence strategies that support survival in unfavorable environments. Among these is sporulation, an ancient bacterial program governed by a large genetic network that requires energy for both regulation and execution. Yet sporulation is a last resort, initiated when cellular energy is nearly exhausted. To resolve this paradox, we quantified the energetic cost of sporulation in units of ATP by integrating time-resolved genome, transcriptome, and proteome profiles. The full cost of the spore cycle, including both formation and revival, ranks among the most energy-intensive processes in the bacterial cell, requiring almost 1010 ATP and consuming about 10% of the total energy budget. The majority of this cost arises from translation, membrane synthesis, and protein turnover. Despite its considerable upfront investment, sporulation enables long-term survival and becomes optimal when harsh conditions extend over timescales of months or longer. This trade-off between immediate cost and delayed benefit helps explain when sporulation is maintained or replaced by alternative strategies. By incorporating our estimates into mechanistic models, we show how metabolic constraints shape sporulation efficiency, while genome-wide mutation accumulation data reveal that even modest energetic burdens can become visible to selection, influencing the evolutionary fate of this complex and widespread trait.
- organism: bacteria (other)
- assay: whole-genome sequencing
- assay: bulk RNA-seq
- assay: proteomics
- research focus: metabolism & bioenergetics
- research focus: chromatin & epigenetic regulation
- research focus: phylogenetics / evolution
- approach: statistical model
- approach: mechanistic / dynamical model
- Citations
- 7
- FWCI
- 23.74
- Field percentile
- top 1%
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