Abstract
Optimal metabolic trade-offs between growth and productivity are key constraints in strain optimization by metabolic engineering; however, how cellular noise impacts these trade-offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown. Here, we introduce a single-cell strategy for quantifying the trade-offs between triacylglycerol production and growth in the oleaginous microorganism Yarrowia lipolytica. The strategy relies on high-throughput quantitative-phase imaging and, enabled by nanoscale secondary ion mass spectrometry analyses and dedicated image processing, allows us to image how resources are partitioned between growth and productivity. Enhanced precision over population-averaging biotechnologies and conventional microscopy demonstrates how cellular noise impacts growth and productivity differently. As such, subpopulations with distinct metabolic trade-offs emerge, with notable impacts on strain performance and robustness. By quantifying the self-degradation of cytosolic macromolecules under nutrient-limiting conditions, we discover the cell-to-cell heterogeneity in protein and fatty-acid recycling, unmasking a potential bet-hedging strategy under starvation.
Highlights
Optimal metabolic trade-offs between growth and productivity are key constraints in strain optimization by metabolic engineering; how cellular noise impacts these trade-offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown
We hypothesized that the cell cytosol is primarily comprised of proteins and nucleic acids, dispersed with lipid droplets (LDs) that are loaded with TAGs at a negligible protein content
We confirmed this hypothesis by characterizing the cytosolic and LD elemental composition with NanoSIMS32
Summary
Optimal metabolic trade-offs between growth and productivity are key constraints in strain optimization by metabolic engineering; how cellular noise impacts these trade-offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown. Similar to a Pareto front[2], these trade-offs can be non-optimal with potential implications for the maintenance of genetic diversity[3] and co-optimization of production titers and yields by metabolic engineering[4] While these trade-offs are challenging to predict given the underlying interactions between distal and often seemingly unrelated genes[5], biotechnologies, such as mass spectrometry, can quantify how resources are allocated to growth and the production of specific metabolites[6]. These screening approaches, operate in a populationaveraging mode, and cannot detect the presence of metabolic subpopulations that can emerge due to cellular noise and cytosolic stochastic phenomena[7,8,9,10,11]. Y. lipolytica has recently attracted substantial attention due to its compatibility with genetic engineering and innate capability to accumulate substantial amounts of TAGs6,30,31
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