A Comparison of the Costs and Benefits of Bacterial Gene Expression

Abstract

To study how a bacterium allocates its resources, we compared the costs and benefits of most of the proteins in Escherichia coli K-12 during growth in minimal glucose medium. Proteins that are important for fitness are usually highly expressed, and 95% of these proteins are expressed at above 13 parts per million (ppm). Conversely, proteins that do not measurably benefit the host tend to be weakly expressed, with a median expression of 13 ppm. In aggregate, genes with no detectable benefit account for 31% of protein production, or about 22% if we correct for genetic redundancy. Although some of the apparently unnecessary expression could have subtle benefits in minimal glucose medium, the majority of the burden is due to genes that are important in other conditions. We propose that over 10% of the cell's protein is "on standby" in case conditions change.

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

“…In this respect, cyanobacterial phototrophic growth is an ideal test case because the regular and periodic environmental changes allow us to formulate the global resource allocation problem in a welldefined way. As yet, similar efforts to investigate proteome allocation have primarily focused on heterotrophic organisms in time-independent environments (21,24,41). These recent analyses reveal several interesting differences with respect to our results.…”

“…This finding was attributed to cellular "bet hedging" in (generalist) wild-type E. coli against unknown environmental challenges. Furthermore, significant protein production without detectable growth benefit was observed experimentally (21,41). In contrast, the model-derived maximal growth rates reported here were within the range of the (maximal) growth rates observed for S. elongatus PCC 7942 [even though actual growth rates observed in a laboratory are typically significantly slower (42)].…”

Principles of proteome allocation are revealed using proteomic data and genome-scale models

“…Thus, the sector constraints reflect costs of cellular decisions to over-allocate proteomic resources for purposes other than maximal growth on a minimal medium. For example, the COG categories “carbohydrate transport and metabolism”, “energy production and conversion”, and “translation, ribosomal structure and biogenesis” were enriched for control by the stress response sigma factor σ S (Supplementary Table S1) and could reflect preparation for unfavorable conditions15.…”

“…Using genome-scale models, we show how such proteomics resources can be used to reveal principles underlying proteome allocation. ME models compute growth optimal proteomes consistent with laboratory evolved strains accurately14, but are unable to compute processes that are not directly related to growth (e.g., stress response, preparation for unfavorable conditions)15. In anticipation of environmental change, generalist (wild-type) E. coli allocate a fraction of the proteome to non-growth related functions.…”

“…Collectively, such allocation can be viewed as “hedging” against unknown enviromental challenges14, reflecting the evolutionary history of the organism and its successful survival strategy. Recent studies have estimated that 20% of the expressed proteome confers no direct fitness benefit15.…”