Metabolic heterogeneity in microbial populations

No two living cells are exactly the same. Even cells from a clonal population with identical genomes living in the same environment will express proteins in different numbers simply due to the random nature of the chemistry involved in gene expression. The consequences of this stochastic gene expression are complex and not well understood, especially at the level of large reaction networks like metabolism. Here we investigate how variability in the copy numbers of metabolic enzymes affects how individual cells extract nourishment from their environment and grow. We model independent microbial cells, each with their own set of enzyme copy numbers sampled from experimental distributions, and use flux balance analysis (FBA) to compute the optimal way that each cell can use its metabolic pathways—an approach we dubbed Population FBA. We find that enzyme variability gives rise to a wide distribution of growth rates, and several metabolic phenotypes—subpopulations relying on diverse metabolic pathways. First we use Population FBA in investigating the effects of single cell proteomics data on the metabolic behavior of an in silico E. coli population. We use the latest metabolic reconstruction integrated with transcriptional regulatory data to model realistic cells growing in a glucose minimal medium under aerobic conditions. The modeled population exhibits a broad distribution of growth rates, and principal component analysis was used to identify well-defined subpopulations that differ in terms of their pathway usage. The cells differentiate into slow-growing acetate-secreting cells and fast-growing CO2-secreting cells, and a large population growing at intermediate rates shift from glycolysis to Entner-Doudoroff (ED) pathway usage. Constraints imposed by integrating regulatory data have a large impact on NADH oxidizing pathway usage within the cell. Finally we find that stochasticity in the expression of only a few genes may be sufficient to capture most of the metabolic variability of the entire population. Next, we extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively. The comparisons to the 13C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature. The minor secretion fluxes for glycerol and acetate are underestimated by our method, which indicate a need for further refinements to the metabolic model. For yeast cells grown in synthetic defined (SD) medium, the calculated broad distribution of growth rates matches experimental observations from single cell studies, and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways. Fast growing yeast cells perform significant amount of respiration, use serine- glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells. We also investigate the degeneracy of the sets of protein-associated constraints that are necessary to give rise to the growth rate distributions seen experimentally. We find that a core set of 51 constraints are essential but that additional constraints are still necessary to reproduce the observed growth rate distributions in SD medium