eLife (Nov 2023)

Linking genotypic and phenotypic changes in the E. coli long-term evolution experiment using metabolomics

  • John S Favate,
  • Kyle S Skalenko,
  • Eric Chiles,
  • Xiaoyang Su,
  • Srujana Samhita Yadavalli,
  • Premal Shah

DOI
https://doi.org/10.7554/eLife.87039
Journal volume & issue
Vol. 12

Abstract

Read online

Changes in an organism’s environment, genome, or gene expression patterns can lead to changes in its metabolism. The metabolic phenotype can be under selection and contributes to adaptation. However, the networked and convoluted nature of an organism’s metabolism makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the long-term evolution experiment (LTEE) with E. coli as a model to understand how mutations can eventually affect metabolism and perhaps fitness. We used mass spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines. We combined this metabolic data with mutation and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work provides a better understanding of how mutations might affect fitness through the metabolic changes in the LTEE and thus provides a major step in developing a complete genotype–phenotype map for this experimental system.

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