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Title: Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments}
Article:
Abstract:
The costs and benefits of protein expression are balanced through evolution. Expression of un-utilized protein( that have no benefits in the current environment) incurs a quantifiable fitness costs on cellular growth rates; however, the magnitude and variability of un-utilized protein expression in natural settings is unknown, largely due to the challenge in determining environment-specific proteome utilization. We address this challenge using absolute and global proteomics data combined with a recently developed genome-scale model of Escherichia coli that computes the environment-specific cost and utility of the proteome on a per gene basis. We show that nearly half of the proteome mass is unused in certain environments and accounting for the cost of this unused protein expression explains >95% of the variance in growth rates of Escherichia coli across 16 distinct environments. Furthermore, reduction in unused protein expression is shown to be a common mechanism to increase cellular growth rates in adaptive evolution experiments. Classification of the unused protein reveals that the unused protein encodes several nutrient- and stress- preparedness functions, which may convey fitness benefits in varying environments. Thus, unused protein expression is the source of large and pervasive fitness costs that may provide the benefit of hedging against environmental change.
Introduction:
The costs and benefits of protein synthesis on organismal fitness shape the evolution and regulation of proteome expression[1]. For microbes, the rate of cellular growth is an important component of organismal fitness[2], and much of the microbial proteome is devoted to growth in certain environments[3]. Protein costs and benefits are often quantified by their effects on cellular growth[1, 4]. Notably, the synthetic overexpression of unused protein results in a predictable linear reduction in growth rates[4, 5]. Microbes will express some amount of unused protein in any given environment, which will reduce their growth rates[6]. However, the magnitude and variability of unused protein expression is unknown, largely due to the challenge in determining environment-specific proteome activity. Thus, we do not yet have a baseline understanding of the percentage of the microbial proteome that is un-utilized in a given environment or how much the un-utilized protein fraction can vary( or if it remains predominantly constant across environments). Here, we combine a recently developed genome-scale model of proteome allocation in Escherichia coli[7] with genome-scale absolute proteomics measurements to quantify proteome allocation across 16 distinct environments[8]. This approach allows us to enumerate the unused proteome, revealing that the unused protein fraction can vary greatly across environment. We subsequently quantify the cost of unused protein expression and characterize the processes and regulators underlying the change in unused protein expression across environments. In turn, we show that unused protein expression is the source of large and pervasive fitness costs that may provide the benefit of hedging against environmental change.
Discussion:
Since the first growth rate model by Monod[25], the quantitative determinants of microbial growth rates have been under investigation. As unused protein expression has been shown to have a causal role in decreasing growth rates in synthetic protein over-expression studies[4, 5], microbial growth rates across different environments may largely be determined by unused protein expression as well. In support of this, we have shown that: 1) quantifying the cost of unused protein expression across several growth conditions explains the variability in growth rates across those environments( Fig 2), 2) strains selected for fast growth rates consistently decrease unused protein expression( Fig 3), and 3) the global regulator CRP predominantly regulates unused protein( across the minimal media environments examined) and the activity of the global regulator correlates with predicted unused protein expression levels( Fig 6). Thus, efforts to engineer strains for bioprocessing applications may benefit from directly eliminating highly expressed unused protein[6], utilizing adaptive evolution experiments to eliminate unused protein[18], or targeting transcription factors known to regulate unused protein[26]. While genome-scale models of metabolism are capable of accurate predictions of growth yields( gram dry weight per gram substrate)[27, 28], predictions of growth rates by have only resulted in moderate correlations( Fig 2B)[7, 29, 30]. Our results suggest that unused protein expression is a key source of growth rate variability that has not been accounted for in genome-scale models to date and has limited the accuracy of growth rate predictions. Incorporation of the regulation of unused protein into genome-scale models may enable more accurate growth rate predictions[23, 31]. We do not intend to imply that local rate-limiting bottlenecks in metabolic flux do not limit growth rates. Rather, these local bottlenecks and the global proteome allocation to unused protein can simultaneously limit growth rates; alleviation of either global or local bottlenecks can increase cellular growth rates. In fact, due to a cell’s native sensing and regulatory mechanisms, alleviation of local bottlenecks may naturally also result in reduction in unused protein expression( and vice versa). As an example, a mutation in glycerol kinase, glpK, alleviates a local rate-limiting step in glycerol metabolism during growth on glycerol[32]; this mutation also results in lower cAMP levels, which affects the expression of unused protein via its effect on the global transcriptional regulator CRP( Fig 3). On the other hand, a mutation in the RNA polymerase subunit rpoB( which can be considered a global regulator), results in the down-regulation of several un-utilized genes( mostly related to stress resistance) and the subsequent up-regulation of genes related to cellular growth[18]. Both mutations in global regulators and local rate-limiting bottlenecks are often found in adaptive evolution experiments[17, 32–34], suggesting that alleviation of both local and global bottlenecks are viable mechanisms to increase cellular growth. While overexpression of an under-utilized core proteome incurs a fitness cost to steady-state growth( Fig 2D), the expression pattern of the core proteome( Fig 5) points to potential fitness benefits. The over-expression of the core proteome may enable a fitness benefit upon encountering more favorable growth environments that can support faster growth rates( Fig 8B). Rather than depending solely on de novo protein synthesis to meet the demands of faster growth, an over-expressed core proteome can enable faster adaptation. To demonstrate this effect, we simulate growth upon shifting from the lowest growth carbon source profiled( galactose) to the highest( glucose) with the ME-Model. The expressed proteome before the environmental shift is used to constrain the growth phenotype immediately following the shift( see Methods). If the core proteome is expressed in excess when growing in galactose, the organism grows faster upon the environmental ‘up-shift’; otherwise, the maximum instantaneous growth rate on glucose will be the same as that on galactose. This fitness benefit upon environmental up-shifts is consistent with experiments showing protein over-expression to incur a higher fitness cost upon environmental up-shifts( where the over-expressed core proteome becomes important for growth in the new environment) than down-shifts[35]. While overexpression of the non-core( largely un-utilized) proteome segments incurs a fitness cost to steady-state growth( Fig 2C), the expression pattern of the C-proteome( Fig 6) points to potential fitness benefits. The expression of the non-core proteome likely enables readiness for environmental change( Fig 8C). To demonstrate this effect, we simulate nutrient supplementations under C- and N-limitation with the ME-Model. Under C-limitation, supplementation with nitrogen sources provides no fitness benefit. On the other hand, supplementation with alternative sources of the limiting element provides growth advantages( Fig 8C). Thus, expression of the C-proteome provides a fitness benefit under C-limitation but not under N-limitation. While the non-core proteome segments are largely un-unutilized in a current environment, they may provide a fitness advantage upon environmental shifts. Indeed, higher activity of CRP has been shown to result in shorter lag phases at a cost of slower growth rates resulting in higher levels of catabolite repression to be beneficial in variable environments[36]. The previous sections suggest that the expression of unused protein can be attributed to proteome segments whose expression incurs a fitness cost on steady-state growth but also confers fitness benefits in varying environments. Here, we summarize the model-defined proteome segments and their regulation with growth rate under variation in carbon source identity and availability. The proteome devoted to cellular growth in these minimal media environments is primarily composed of an invariant core proteome( Figs 4 and 5). Also contributing to growth is smaller portions of the non-core ME( Fig 4B) and non-ME proteomes( Fig 7C). The remaining non-core ME proteome can be considered to be devoted to nutrient readiness( Fig 6); these proteins can be utilized for growth when alternative nutrients become available. The non-ME proteome that is not devoted to cellular growth is predominantly related to stress functions( Fig 7C). Thus, quantifying the utilization and function of the proteome reveals its allocation towards growth, nutrient readiness, and stress resistance. At low growth rates, nutrient readiness and stress resistance functions are more abundant; at higher growth rates, allocation to these functions decreases to enable increased allocation to cellular growth( Fig 8D). Nutrient readiness and stress resistance functions can be considered ‘hedging’ functions that enable readiness for environmental change rather than being actively utilized, resulting in trade-offs between growth, nutrient readiness, and stress resistance due to proteome allocation constraints. Indeed, several studies have shown that slower growing cells are more resistant to cellular stresses[37–39]. An important implication of the identified fitness trade-offs is that cellular growth rates may be primarily determined by environmental history, rather than nutrient quality( i. e., the maximum growth rate possible with a specified nutrient, Fig 2A). As growth rates are determined by the proteome allocated towards growth, evolutionary and ecological factors may be more important than the identity and quality of the substrates themselves. For example, while glucose and galactose have similar inherent qualities as sole carbon sources( chemically and as defined by the growth potential of E. coli on these substrates), glucose is the highest growth substrate in this dataset and galactose is the lowest. Galactose may be a more rarely or transiently encountered carbon source or perhaps it is often associated with harsher environments, which would make environmental readiness and stress resistance comparatively more important components of overall cellular fitness[40–42]. The different regulatory patterns( e. g., different slopes and intercepts of the regressions in Figs 5A and 6A) observed across strains[4, 23, 43, 44] may reflect and dictate their ecological strategy and environmental history and be balanced through the activity of global regulators[36, 45]. While we broadly classify the proteome here based on nutrient readiness and stress resistance functions, it is important to realize that within these proteome segments is a variety of distinct functions enabling readiness for specific nutrients and stresses. Concerted up-regulation of these segments results in a general nutritional readiness and stress resistance. However, as an organism becomes prepared for a wider array of nutrients or stresses, these proteome segments must become larger. Therefore, in addition to a trade-off between the different components of organismal fitness identified here( growth, environmental readiness, stress resistance), proteome allocation constraints also result in a trade-off in specialist versus generalist ecological strategies. E. coli is a generalist species, capable of growing in a variety of environments. Its broad environmental niche results in large proteome burden. On the other hand, specialist species( capable of growing on a narrower range of substrates) would require a smaller proteome allocation to be equally ready for environmental change. Proteome allocation constraints will therefore also result in a trade-off between specialist and generalist strategies[46].
An overarching endeavor in systems biology is to characterize and understand the allocation of an organism’s proteome . Common approaches to characterize proteome allocation are based on annotations of protein functions or transcriptional regulatory targets . Here , we develop a novel approach based on model-predicted proteome utilization . This approach reveals that in many environments , a large fraction of the proteome is unused . Unused protein expression is known to incur costs on organismal fitness . We show that changes in the allocation of the proteome to used versus unused fractions can account for the variability in growth rates observed across environments and is a common mechanism to increase growth rates in laboratory evolution experiments . We compare our approach to classify the proteome based on model-predicted utilization to more traditional approaches to reveal biological functions and transcriptional regulators underlying the expression of unused protein . Expression of these functions may reflect ecological trade-offs between growth , nutrient-readiness , and stress resistance .