Limits to the evolution of metabolic dependency in spatially structured microbial communities
Year: 2026
Type: article
Abstract: Summary In microbial communities, evolutionary processes can lead to loss of biosynthetic pathways, creating metabolic dependencies. The Black Queen Hypothesis suggests that such gene loss can confer a fitness advantage by reducing metabolic burden. However, how these dependencies evolve at the level of individual cells in spatially structured communities remains poorly understood. We used a combination of microfluidic single-cell imaging and mathematical modeling to examine the early fate of auxotrophic mutants within E. coli populations. We found that without external amino acids, auxotroph growth is strongly constrained by low amino acid leakage from wildtype neighbors, and further reduced when they form local clusters that drain this limited amino acid pool. A growth advantage was only observed when amino acids were added or when leakage from wildtypes exceeded a threshold. Together, our results reveal insights into determinants of mutant invasion fitness and the trade-offs between reducing metabolic costs and maintaining metabolic autonomy. Highlights Single-cell imaging reveals how auxotrophs fare in spatially structured populations Auxotrophs grow slowly due to low amino acid leakage from wildtype neighbors Mutant clustering intensifies local amino acid depletion and reduces auxotroph growth Model identifies leakage threshold required for auxotroph invasion from rare
Source: iScience
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,240
Awards: G2027283502, NCCR Microbiomes: Microbial communities in health and environment, The role of cell-cell interactions in the development and virulence of multispecies biofilms, NCCR Microbiomes: Microbial communities in health and environment, A bottom-up approach to microbial ecology: scaling up from the behavior of individuals and their interactions to the dynamics of microbial communities
OpenAlex ID: W7128005295
