Protein costs do not explain evolution of metabolic strategies and regulation of ribosomal content: does protein investment explain an anaerobic bacterial Crabtree effect?

Carbon upshift in Lactococcus cremoris elicits immediate initiation of proteome-wide adaptation, coinciding with growth acceleration and pyruvate dissipation switching

Fitness optimization in a dynamic environment requires bacteria to adapt their proteome in a tightly regulated manner by altering protein production and/or degradation. Here, we investigate proteome adaptation in Lactococcus cremoris following a sudden nutrient upshift (e.g., nutrients that allow faster growth) and focus especially on the fate of redundant proteins after the shift. Protein turnover analysis demonstrated that L. cremoris cultures shifted from galactose to glucose, immediately accelerate growth and initiate proteome-wide adjustment toward glucose-optimized composition. Redundant proteins were predominantly adjusted by lowering (or stopping) protein production combined with dilution by growth. However, pyruvate formate lyase activator (PflA) was actively degraded, which appears correlated to reduced 4Fe-4S cofactor availability. Active PflA removal induces the shutdown of galactose-associated mixed acid fermentation to accelerate the switch toward glucose-associated homolactic fermentation. Our work deciphers molecular adjustments upon environmental change that drive physiological adaptation, including growth rate and central energy metabolism.IMPORTANCEBacteria adapt to their environment by adjusting their molecular makeup, in particular their proteome, which ensures fitness optimization under the newly encountered environmental condition. We present a detailed analysis of proteome adaptation kinetics in Lactococcus cremoris following its acute transition from galactose to glucose media, as an example of a sudden nutrient quality upshift. Analysis of the replacement times of individual proteins after the nutrient upshift established that the entire proteome is instantly adjusting to the new condition, which coincides with immediate growth rate acceleration and metabolic adaptation. The latter is driven by the active removal of the pyruvate formate lyase activator protein that is pivotal in controlling pyruvate dissipation in L. cremoris. Our work exemplifies the amazing rate of molecular adaptation in bacteria that underlies physiological adjustments, including growth rate and carbon metabolism. This mechanistic study contributes to our understanding of adaptation in L. cremoris during the dynamic conditions it encounters during (industrial) fermentation, even though environmental transitions in these processes are mostly more gradual than the acute shift studied here.

A framework for understanding collective microbiome metabolism

Microbiome metabolism underlies numerous vital ecosystem functions. Individual microbiome members often perform partial catabolism of substrates or do not express all of the metabolic functions required for growth. Microbiome members can complement each other by exchanging metabolic intermediates and cellular building blocks to achieve a collective metabolism. We currently lack a mechanistic framework to explain why microbiome members adopt partial metabolism and how metabolic functions are distributed among them. Here we argue that natural selection for proteome efficiency-that is, performing essential metabolic fluxes at a minimal protein investment-explains partial metabolism of microbiome members, which underpins the collective metabolism of microbiomes. Using the carbon cycle as an example, we discuss motifs of collective metabolism, the conditions under which these motifs increase the proteome efficiency of individuals and the metabolic interactions they result in. In summary, we propose a mechanistic framework for how collective metabolic functions emerge from selection on individuals.

Biopurification using non-growing microorganisms to improve plant protein ingredients

Securing a sustainable global food supply for a growing population requires a shift toward a more plant-based diet. The application of plant-based proteins is therefore increasing, but unpleasant off-flavors complicate their use. Here, we screened 97 microorganisms for their potential to remove off-flavors in a process with limiting amounts of fermentable sugar. This allowed the production of a more neutral-tasting, purified food ingredient while limiting microbial growth and the production of typical fermentation end products. We demonstrate that various lactic acid bacteria (LAB) and yeasts remove "green" aldehydes and ketones. This conversion can be carried out in less than one hour in almond, pea, potato, and oat proteins. Heterofermentative LAB was best at aldehyde and ketone neutralization with minimum de novo formation of microbial volatiles such as ethylacetate (sweet, fruity) or alpha-diketones (butter- and cheese-like). While sensory properties were improved, changes in protein solubility, emulsification, foaming, and in vitro digestibility were limited.

The stressostat: A novel approach in adaptive laboratory evolution to improve end-product resistance

End-product inhibition in pH-controlled batch cultures, is the major limiting factor for bacterial biomass formation in the starter culture industry as well as in many other biotechnological processes. Adaptive laboratory evolution (ALE) has emerged over the past decades as a powerful tool for phenotype optimization, but none of the existing ALE methods could select for improved end-product resistance. Therefore, we developed the stressostat (STress Resistance Evolution in Substrate Surplus) as a novel continuous ALE method. Stressostat cultivation applies end-product concentrations as constant evolutionary pressure on microorganisms in the presence of substrate surplus. In this study, we improved the lactate resistance of Lactococcus lactis FM03P in 35 days of stressostat cultivations. The lactate concentrations increased over time from 530 to 675 mM, indicating the successful selection for variants with improved lactate resistance. Thirty-four variants were isolated and grouped into four clusters based on their growth rates at high lactate concentrations. In the high-throughput screening without pH control, most isolated variants could grow at high lactate concentrations (870-928 mM), while the wild type was completely inhibited. The variants grew slower than wild type at low lactate media indicating possible evolutionary trade-off. However, in pH-controlled batch cultivations, most variants produced more biomass than the wild type. In conclusion, stressostat cultivation is a valuable method to obtain L. lactis variants with improved end-product resistance and further characterization is needed to elucidate underlying resistance mechanisms and potential industrial applications.

The hierarchy of sugar catabolization in Lactococcus cremoris

IMPORTANCE

The availability of nutrients to microorganisms varies considerably between different environments, and changes can occur rapidly. As a general rule, a fast growth rate-typically growth on glucose-is associated with the repression of other carbohydrate utilization genes, but it is not clear to what extent catabolite repression is exerted by other sugars. We investigated the hierarchy of sugar utilization after substrate transitions in Lactococcus cremoris. For this, we determined the proteome and carbohydrate utilization capacity after growth on different sugars. The results show that the preparedness of cells for the utilization of "slower" sugars is not strictly determined by the growth rate. The data point to individual proteins relevant for various sugar transitions and suggest that the evolutionary history of the organism might be responsible for deviations from a strictly growth rate-related sugar catabolization hierarchy.

Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

Overflow metabolism at the thermodynamic limit of life: How carboxydotrophic acetogens mitigate carbon monoxide toxicity

Carboxydotrophic metabolism is gaining interest due to its applications in gas fermentation technology, enabling the conversion of carbon monoxide to fuels and commodities. Acetogenic carboxydotrophs play a central role in current gas fermentation processes. In contrast to other energy-rich microbial substrates, CO is highly toxic, which makes it a challenging substrate to utilize. Instantaneous scavenging of CO upon entering the cell is required to mitigate its toxicity. Experiments conducted with Clostridium autoethanogenum at different biomass-specific growth rates show that elevated ethanol production occurs at increasing growth rates. The increased allocation of electrons towards ethanol at higher growth rates strongly suggests that C. autoethanogenum employs a form of overflow metabolism to cope with high dissolved CO concentrations. We argue that this overflow branch enables acetogens to efficiently use CO at highly variable substrate influxes by increasing the conversion rate almost instantaneously when required to remove toxic substrate and promote growth. In this perspective, we will address the case study of C. autoethanogenum grown solely on CO and syngas mixtures to assess how it employs acetate reduction to ethanol as a form of overflow metabolism.

Engineering proton-coupled hexose uptake in Saccharomyces cerevisiae for improved ethanol yield

Background

In the yeast Saccharomyces cerevisiae, which is widely applied for industrial bioethanol production, uptake of hexoses is mediated by transporters with a facilitated diffusion mechanism. In anaerobic cultures, a higher ethanol yield can be achieved when transport of hexoses is proton-coupled, because of the lower net ATP yield of sugar dissimilation. In this study, the facilitated diffusion transport system for hexose sugars of S. cerevisiae was replaced by hexose–proton symport.

Results

Introduction of heterologous glucose– or fructose–proton symporters in an hxt⁰ yeast background strain (derived from CEN.PK2-1C) restored growth on the corresponding sugar under aerobic conditions. After applying an evolutionary engineering strategy to enable anaerobic growth, the hexose–proton symporter-expressing strains were grown in anaerobic, hexose-limited chemostats on synthetic defined medium, which showed that the biomass yield of the resulting strains was decreased by 44.0-47.6%, whereas the ethanol yield had increased by up to 17.2% (from 1.51 to 1.77 mol mol hexose⁻¹) compared to an isogenic strain expressing the hexose uniporter HXT5. To apply this strategy to increase the ethanol yield on sucrose, we constructed a platform strain in which all genes encoding hexose transporters, disaccharide transporters and disaccharide hydrolases were deleted, after which a combination of a glucose–proton symporter, fructose–proton symporter and extracellular invertase (SUC2) were introduced. After evolution, the resulting strain exhibited a 16.6% increased anaerobic ethanol yield (from 1.51 to 1.76 mol mol hexose equivalent⁻¹) and 46.6% decreased biomass yield on sucrose.

Conclusions

This study provides a proof-of-concept for the replacement of the endogenous hexose transporters of S. cerevisiae by hexose-proton symport, and the concomitant decrease in ATP yield, to greatly improve the anaerobic yield of ethanol on sugar. Moreover, the sugar-negative platform strain constructed in this study acts as a valuable starting point for future studies on sugar transport or development of cell factories requiring specific sugar transport mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02145-7.

An excess of glycolytic enzymes under glucose-limited conditions may enable Saccharomyces cerevisiae to adapt to nutrient availability

Microorganisms, including the budding yeast Saccharomyces cerevisiae, express glycolytic proteins to a maximal capacity that (largely) exceeds the actual flux through the enzymes, especially at low growth rates. An open question is if this apparent expression level is really an overcapacity, or maintains the (optimal) enzyme capacity needed to carry flux at (very) low substrate availability. Here, we use computational modelling to suggest that yeast maintains a genuine excess of glycolytic enzymes at low specific growth rates. During fast fermentative growth at high glucose levels, the observed expression of the glycolytic enzymes matched the predicted optimal levels. We suggest that the excess glycolytic capacity at low glucose levels is a preparatory strategy in the adaptation to sugar fluctuations in the environment.

Time-Optimal Adaptation in Metabolic Network Models

Analysis of metabolic models using constraint-based optimization has emerged as an important computational technique to elucidate and eventually predict cellular metabolism and growth. In this work, we introduce time-optimal adaptation (TOA), a new constraint-based modeling approach that allows us to evaluate the fastest possible adaptation to a pre-defined cellular state while fulfilling a given set of dynamic and static constraints. TOA falls into the mathematical problem class of time-optimal control problems, and, in its general form, can be broadly applied and thereby extends most existing constraint-based modeling frameworks. Specifically, we introduce a general mathematical framework that captures many existing constraint-based methods and define TOA within this framework. We then exemplify TOA using a coarse-grained self-replicator model and demonstrate that TOA allows us to explain several well-known experimental phenomena that are difficult to explore using existing constraint-based analysis methods. We show that TOA predicts accumulation of storage compounds in constant environments, as well as overshoot uptake metabolism after periods of nutrient scarcity. TOA shows that organisms with internal temporal degrees of freedom, such as storage, can in most environments outperform organisms with a static intracellular composition. Furthermore, TOA reveals that organisms adapted to better growth conditions than present in the environment (“optimists”) typically outperform organisms adapted to poorer growth conditions (“pessimists”).

Manganese Modulates Metabolic Activity and Redox Homeostasis in Translationally Blocked Lactococcus cremoris, Impacting Metabolic Persistence, Cell Culturability, and Flavor Formation

Manganese (Mn) is an essential trace element that is supplemented in microbial media with varying benefits across species and growth conditions. We found that growth of Lactococcus cremoris was unaffected by manganese omission from the growth medium. The main proteome adaptation to manganese omission involved increased manganese transporter production (up to 2,000-fold), while the remaining 10 significant proteome changes were between 1.4- and 4-fold. Further investigation in translationally blocked (TB), nongrowing cells showed that Mn supplementation (20 μM) led to approximately 1.5 X faster acidification compared with Mn-free conditions. However, this faster acidification stagnated within 24 h, likely due to draining of intracellular NADH that coincides with substantial loss of culturability. Conversely, without manganese, nongrowing cells persisted to acidify for weeks, albeit at a reduced rate, but maintaining redox balance and culturability. Strikingly, despite being unculturable, α-keto acid-derived aldehydes continued to accumulate in cells incubated in the presence of manganese, whereas without manganese cells predominantly formed the corresponding alcohols. This is most likely reflecting NADH availability for the alcohol dehydrogenase-catalyzed conversion. Overall, manganese influences the lactococcal acidification rate, and flavor formation capacity in a redox dependent manner. These are important industrial traits especially during cheese ripening, where cells are in a non-growing, often unculturable state.

IMPORTANCE In nature as well as in various biotechnology applications, microorganisms are often in a nongrowing state and their metabolic persistence determines cell survival and functionality. Industrial examples are dairy fermentations where bacteria remain active during the ripening phases that can take up to months and even years. Here we investigated environmental factors that can influence lactococcal metabolic persistence throughout such prolonged periods. We found that in the absence of manganese, acidification of nongrowing cells remained active for weeks while in the presence of manganese it stopped within 1 day. The latter coincided with the accumulation of amino acid derived volatile metabolites. Based on metabolic conversions, proteome analysis, and a reporter assay, we demonstrated that the manganese elicited effects were NADH dependent. Overall the results show the effect of environmental modulation on prolonged cell-based catalysis, which is highly relevant to non-growing cells in nature and biotechnological applications.

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation—known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells.

Metabolically active organelles compete for cytosolic space and resources during metabolism rewiring. Here, the authors develop a computational model of yeast metabolism and resource allocation to predict condition- and compartment-specific proteome constraints that govern metabolic strategies.

Understanding FBA Solutions under Multiple Nutrient Limitations

Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret. This is especially the case for conditions with many constraints, such as for organisms growing in rich nutrient environments: it remains unclear why a certain solution was optimal. Here, we rationalize FBA solutions by explaining for which properties the optimal combination of metabolic strategies is selected. We provide a graphical formalism in which the selection of solutions can be visualized; we illustrate how this perspective provides a glimpse of the logic that underlies genome-scale modeling by applying our formalism to models of various sizes.

An alternative resource allocation strategy in the chemolithoautotrophic archaeon Methanococcus maripaludis

Most microorganisms in nature spend the majority of time in a state of slow or zero growth and slow metabolism under limited energy or nutrient flux rather than growing at maximum rates. Yet, most of our knowledge has been derived from studies on fast-growing bacteria. Here, we systematically characterized the physiology of the methanogenic archaeon Methanococcus maripaludis during slow growth. M. maripaludis was grown in continuous culture under energy (formate)-limiting conditions at different dilution rates ranging from 0.09 to 0.002 h^-1, the latter corresponding to 1% of its maximum growth rate under laboratory conditions (0.23 h^-1). While the specific rate of methanogenesis correlated with growth rate as expected, the fraction of cellular energy used for maintenance increased and the maintenance energy per biomass decreased at slower growth. Notably, proteome allocation between catabolic and anabolic pathways was invariant with growth rate. Unexpectedly, cells maintained their maximum methanogenesis capacity over a wide range of growth rates, except for the lowest rates tested. Cell size, cellular DNA, RNA, and protein content as well as ribosome numbers also were largely invariant with growth rate. A reduced protein synthesis rate during slow growth was achieved by a reduction in ribosome activity rather than via the number of cellular ribosomes. Our data revealed a resource allocation strategy of a methanogenic archaeon during energy limitation that is fundamentally different from commonly studied versatile chemoheterotrophic bacteria such as E. coli.

Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments

Cells adapt to different conditions via gene expression that tunes metabolism for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade-offs. Resource allocation under proteome constraints has explained regulatory strategies in bacteria. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient-rich conditions, i.e., multiple available nitrogen sources, such as Lactococcus lactis. Here, we present a proteome-constrained genome-scale metabolic model of L. lactis (pcLactis) to interpret growth on multiple nutrients. Through integration of proteomics and flux data, in glucose-limited chemostats, the model predicted glucose and arginine uptake as dominant constraints at low growth rates. Indeed, glucose and arginine catabolism were found upregulated in evolved mutants. At high growth rates, pcLactis correctly predicted the observed shutdown of arginine catabolism because limited proteome availability favored lactate for ATP production. Thus, our model-based analysis is able to identify and explain the proteome constraints that limit growth rate in nutrient-rich environments and thus form targets of fitness improvement.

Global coordination of metabolic pathways in Escherichia coli by active and passive regulation

Microorganisms adjust metabolic activity to cope with diverse environments. While many studies have provided insights into how individual pathways are regulated, the mechanisms that give rise to coordinated metabolic responses are poorly understood. Here, we identify the regulatory mechanisms that coordinate catabolism and anabolism in Escherichia coli. Integrating protein, metabolite, and flux changes in genetically implemented catabolic or anabolic limitations, we show that combined global and local mechanisms coordinate the response to metabolic limitations. To allocate proteomic resources between catabolism and anabolism, E. coli uses a simple global gene regulatory program. Surprisingly, this program is largely implemented by a single transcription factor, Crp, which directly activates the expression of catabolic enzymes and indirectly reduces the expression of anabolic enzymes by passively sequestering cellular resources needed for their synthesis. However, metabolic fluxes are not controlled by this regulatory program alone; instead, fluxes are adjusted mostly through passive changes in the local metabolite concentrations. These mechanisms constitute a simple but effective global regulatory program that coarsely partitions resources between different parts of metabolism while ensuring robust coordination of individual metabolic reactions.

Carbon Source-Dependent Reprogramming of Anaerobic Metabolism in Staphylococcus aureus

To be a successful pathogen, Staphylococcus aureus has to adapt its metabolism to the typically oxygen- and glucose-limited environment of the host. Under fermenting conditions and in the presence of glucose, S. aureus uses glycolysis to generate ATP via substrate-level phosphorylation and mainly lactic acid fermentation to maintain the redox balance by reoxidation of NADH equivalents. However, it is less clear how S. aureus proceeds under anoxic conditions and glucose limitation, likely representing the bona fide situation in the host. Using a combination of proteomic, transcriptional, and metabolomic analyses, we show that in the absence of an abundant glycolysis substrate, the available carbon source pyruvate is converted to acetyl coenzyme A (AcCoA) in a pyruvate formate-lyase (PflB)-dependent reaction to produce ATP and acetate. This process critically depends on derepression of the catabolite control protein A (CcpA), leading to upregulation of pflB transcription. Under these conditions, ethanol production is repressed to prevent wasteful consumption of AcCoA. In addition, our global and quantitative characterization of the metabolic switch prioritizing acetate over lactate fermentation when glucose is absent illustrates examples of carbon source-dependent control of colonization and pathogenicity factors.IMPORTANCE Under infection conditions, S. aureus needs to ensure survival when energy production via oxidative phosphorylation is not possible, e.g., either due to the lack of terminal electron acceptors or by the inactivation of components of the respiratory chain. Under these conditions, S. aureus can switch to mixed-acid fermentation to sustain ATP production by substrate level phosphorylation. The drop in the cellular NAD⁺/NADH ratio is sensed by the repressor Rex, resulting in derepression of fermentation genes. Here, we show that expression of fermentation pathways is further controlled by CcpA in response to the availability of glucose to ensure optimal resource utilization under growth-limiting conditions. We provide evidence for carbon source-dependent control of colonization and virulence factors. These findings add another level to the regulatory network controlling mixed-acid fermentation in S. aureus and provide additional evidence for the lifestyle-modulating effect of carbon sources available to S. aureus.

Lifestyle, metabolism and environmental adaptation in Lactococcus lactis

Lactococcus lactis serves as a paradigm organism for the lactic acid bacteria (LAB). Extensive research into the molecular biology, metabolism and physiology of several model strains of this species has been fundamental for our understanding of the LAB. Genomic studies have provided new insights into the species L. lactis, including the resolution of the genetic basis of its subspecies division, as well as the control mechanisms involved in the fine-tuning of growth rate and energy metabolism. In addition, it has enabled novel approaches to study lactococcal lifestyle adaptations to the dairy application environment, including its adjustment to near-zero growth rates that are particularly relevant in the context of cheese ripening. This review highlights various insights in these areas and exemplifies the strength of combining experimental evolution with functional genomics and bacterial physiology research to expand our fundamental understanding of the L. lactis lifestyle under different environmental conditions.

Microbial inefficient substrate use through the perspective of resource allocation models

Microorganisms extract energy from substrates following strategies that may seem suboptimal at first glance. Beyond the so-called yield-rate trade-off, resource allocation models, which focus on assigning different functional roles to the limited number of enzymes that a cell can support, offer a framework to interpret the inefficient substrate use by microorganisms. We review here relevant examples of substrate conversions where a significant part of the available energy is not utilised and how resource allocation models offer a mechanistic interpretation thereof, notably for open mixed cultures. Future developments are identified, in particular, the challenge of considering metabolic flexibility towards uncertain environmental changes instead of strict fixed optimality objectives, with the final goal of increasing the prediction capabilities of resource allocation models. Finally, we highlight the relevance of resource allocation to understand and enable a promising biorefinery platform revolving around lactate, which would increase the flexibility of waste-to-chemical biorefinery schemes.

Resource allocation explains lactic acid production in mixed-culture anaerobic fermentations

Lactate production in anaerobic carbohydrate fermentations with mixed cultures of microorganisms is generally observed only in very specific conditions: the reactor should be run discontinuously and peptides and B vitamins must be present in the culture medium as lactic acid bacteria (LAB) are typically auxotrophic for amino acids. State-of-the-art anaerobic fermentation models assume that microorganisms optimise the adenosine triphosphate (ATP) yield on substrate and therefore they do not predict the less ATP efficient lactate production, which limits their application for designing lactate production in mixed-culture fermentations. In this study, a metabolic model taking into account cellular resource allocation and limitation is proposed to predict and analyse under which conditions lactate production from glucose can be beneficial for microorganisms. The model uses a flux balances analysis approach incorporating additional constraints from the resource allocation theory and simulates glucose fermentation in a continuous reactor. This approach predicts lactate production is predicted at high dilution rates, provided that amino acids are in the culture medium. In minimal medium and lower dilution rates, mostly butyrate and no lactate is predicted. Auxotrophy for amino acids of LAB is identified to provide a competitive advantage in rich media because less resources need to be allocated for anabolic machinery and higher specific growth rates can be achieved. The Matlab™ codes required for performing the simulations presented in this study are available at https://doi.org/10.5281/zenodo.4031144.

A Novel Method for Long-Term Analysis of Lactic Acid and Ammonium Production in Non-growing Lactococcus lactis Reveals Pre-culture and Strain Dependence

In various (industrial) conditions, cells are in a non-growing but metabolically active state in which de novo protein synthesis capacity is limited. The production of a metabolite by such non-growing cells is dependent on the cellular condition and enzyme activities, such as the amount, stability, and degradation of the enzyme(s). For industrial fermentations in which the metabolites of interest are mainly formed after cells enter the stationary phase, the investigation of prolonged metabolite production is of great importance. However, current batch model systems do not allow prolonged measurements due to metabolite accumulation driving product-inhibition. Here we developed a protocol that allows high-throughput metabolic measurements to be followed in real-time over extended periods (weeks). As a validation model, sugar utilization and arginine consumption by a low density of translationally blocked Lactococcus lactis was designed in a defined medium. In this system L. lactis MG1363 was compared with its derivative HB60, a strain described to achieve higher metabolic yield through a shift toward heterofermentative metabolism. The results showed that in a non-growing state HB60 is able to utilize more arginine than MG1363, and for both strains the decay of the measured activities were dependent on pre-culture conditions. During the first 5 days of monitoring a ∼25-fold decrease in acidification rate was found for strain HB60 as compared to a ∼20-fold decrease for strain MG1363. Such measurements are relevant for the understanding of microbial metabolism and for optimizing applications in which cells are frequently exposed to long-term suboptimal conditions, such as microbial cell factories, fermentation ripening, and storage survival.

Genome-scale modeling for Bacillus coagulans to understand the metabolic characteristics

Lactic acid is widely used in many industries, especially in the production of poly-lactic acid. Bacillus coagulans is a promising lactic acid producer in industrial fermentation due to its thermophilic property. In this study, we developed the first genome-scale metabolic model (GEM) of B. coagulans iBag597, together with an enzyme-constrained model ec-iBag597. We measured strain-specific biomass composition and integrated the data into a biomass equation. Then, we validated iBag597 against experimental data generated in this study, including amino acid requirements and carbon source utilization, showing that simulations were generally consistent with the experimental results. Subsequently, we carried out chemostats to investigate the effects of specific growth rate and culture pH on metabolism of B. coagulans. Meanwhile, we used iBag597 to estimate the intracellular metabolic fluxes for those conditions. The results showed that B. coagulans was capable of generating ATP via multiple pathways, and switched among them in response to various conditions. With ec-iBag597, we estimated the protein cost and protein efficiency for each ATP-producing pathway to investigate the switches. Our models pave the way for systems biology of B. coagulans, and our findings suggest that maintaining a proper growth rate and selecting an optimal pH are beneficial for lactate fermentation.

A yield-cost tradeoff governs Escherichia coli's decision between fermentation and respiration in carbon-limited growth

Living cells react to changes in growth conditions by re-shaping their proteome. This accounts for different stress-response strategies, both specific (i.e., aimed at increasing the availability of stress-mitigating proteins) and systemic (such as large-scale changes in the use of metabolic pathways aimed at a more efficient exploitation of resources). Proteome re-allocation can, however, imply significant biosynthetic costs. Whether and how such costs impact the growth performance are largely open problems. Focusing on carbon-limited E. coli growth, we integrate genome-scale modeling and proteomic data to address these questions at quantitative level. After deriving a simple formula linking growth rate, carbon intake, and biosynthetic costs, we show that optimal growth results from the tradeoff between yield maximization and protein burden minimization. Empirical data confirm that E. coli growth is indeed close to Pareto-optimal over a broad range of growth rates. Moreover, we establish that, while most of the intaken carbon is diverted into biomass precursors, the efficiency of ATP synthesis is the key driver of the yield-cost tradeoff. These findings provide a quantitative perspective on carbon overflow, the origin of growth laws and the multidimensional optimality of E. coli metabolism.

Systems Biology - A Guide for Understanding and Developing Improved Strains of Lactic Acid Bacteria

Lactic Acid Bacteria (LAB) are extensively employed in the production of various fermented foods, due to their safe status, ability to affect texture and flavor and finally due to the beneficial effect they have on shelf-life. More recently, LAB have also gained interest as production hosts for various useful compounds, particularly compounds with sensitive applications, such as food ingredients and therapeutics. As for all industrial microorganisms, it is important to have a good understanding of the physiology and metabolism of LAB in order to fully exploit their potential, and for this purpose, many systems biology approaches are available. Systems metabolic engineering, an approach that combines optimization of metabolic enzymes/pathways at the systems level, synthetic biology as well as in silico model simulation, has been used to build microbial cell factories for production of biofuels, food ingredients and biochemicals. When developing LAB for use in foods, genetic engineering is in general not an accepted approach. An alternative is to screen mutant libraries for candidates with desirable traits using high-throughput screening technologies or to use adaptive laboratory evolution to select for mutants with special properties. In both cases, by using omics data and data-driven technologies to scrutinize these, it is possible to find the underlying cause for the desired attributes of such mutants. This review aims to describe how systems biology tools can be used for obtaining both engineered as well as non-engineered LAB with novel and desired properties.

Application of a partial cell recycling chemostat for continuous production of aroma compounds at near-zero growth rates

Objective

The partial cell recycling chemostat is a modification of the chemostat in which cells are partially recycled towards the bioreactor. This allows using dilution rates higher than the maximum growth rate resulting in higher biomass concentrations and increased process rates. In this study, we demonstrate with a single observation that this system can also be used to study microorganisms at near-zero growth rates and as production system for compounds specific for slow growth, such as those typical for ripened cheese.

Results

Lactococcus lactis FM03-V2 was cultivated at growth rates between 0.0025 and 0.025 h⁻¹. Detailed analysis of produced aroma compounds revealed that levels of particular compounds were clearly affected by the growth rate within the studied range demonstrating that we can steer the aroma production by controlling the growth rate. With this approach, we also experimentally validated that the maintenance coefficient of this dairy strain decreased at near-zero growth rates (6.4-fold). An exponentially decreasing maintenance coefficient was included in the growth model, enabling accurate prediction of biomass accumulation in the partial cell recycling chemostat. This study demonstrates the potential of partial cell recycling chemostat both as aroma production system at near-zero growth rates and as unique research tool.

Electronic supplementary material

The online version of this article (10.1186/s13104-019-4213-4) contains supplementary material, which is available to authorized users.

The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates

Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized-in a static environment under mass-conservation and enzyme expression constraints-we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.

Modelling overflow metabolism in Escherichia coli with flux balance analysis incorporating differential proteomic efficiencies of energy pathways

Background

The formation of acetate by fast-growing Escherichia coli (E. coli) is a commonly observed phenomenon, often referred to as overflow metabolism. Among various studies that have been carried over decades, a recent work (Basan, M. et al. Nature528, 99–104, 2015) suggested and validated that it is the differential proteomic efficiencies in energy biogenesis between fermentation and respiration that lead to the production of acetate at rapid growth conditions, as the consequence of optimally allocating the limited proteomic resource. In the current work, we attempt to incorporate this newly developed proteome allocation theory into flux balance analysis (FBA) to capture quantitatively the extent of overflow metabolism in different E. coli strains.

Results

A concise constraint was introduced into a FBA-based model with three proteomic cost parameters to represent constrained allocation of proteome over two energy (respiration and fermentation) pathways and biomass synthesis. Linear relationships were shown to exist between the three proteomic cost parameters. Tests with three different strains revealed that the proteomic cost of fermentation was consistently lower than that of respiration. A slow-growing strain appeared to have a higher proteomic cost for biomass synthesis than fast-growing strains. Different assumed levels of carbon flowing into pentose phosphate pathway affected the absolute value of model parameters, but had no qualitative impact on the comparative proteomic costs. For the prediction of biomass yield, significant errors that occurred for one of the tested strains (ML308) were rectified by adjusting the cellular energy demand according to literature data.

Conclusions

With the aid of a concise proteome allocation constraint, our FBA-based model is able to quantitatively predict the onset and extent of the overflow metabolism in various E. coli strains. Such prediction is enabled by three linearly-correlated (as opposed to uniquely determinable) proteomic cost parameters. The linear relationships between these parameters, when determined using data from cell culturing experiments, render biologically meaningful comparative proteomic costs between fermentation and respiration pathways and between the biomass synthesis sectors of slow- and fast-growing species. Simultaneous prediction of acetate production and biomass yield in the overflow region requires the use of reliable cellular energy demand data.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0677-4) contains supplementary material, which is available to authorized users.

Adaption to glucose limitation is modulated by the pleotropic regulator CcpA, independent of selection pressure strength

Background

A central theme in (micro)biology is understanding the molecular basis of fitness i.e. which strategies are successful under which conditions; how do organisms implement such strategies at the molecular level; and which constraints shape the trade-offs between alternative strategies. Highly standardized microbial laboratory evolution experiments are ideally suited to approach these questions. For example, prolonged chemostats provide a constant environment in which the growth rate can be set, and the adaptive process of the organism to such environment can be subsequently characterized.

Results

We performed parallel laboratory evolution of Lactococcus lactis in chemostats varying the quantitative value of the selective pressure by imposing two different growth rates. A mutation in one specific amino acid residue of the global transcriptional regulator of carbon metabolism, CcpA, was selected in all of the evolution experiments performed. We subsequently showed that this mutation confers predictable fitness improvements at other glucose-limited growth rates as well. In silico protein structural analysis of wild type and evolved CcpA, as well as biochemical and phenotypic assays, provided the underpinning molecular mechanisms that resulted in the specific reprogramming favored in constant environments.

Conclusion

This study provides a comprehensive understanding of a case of microbial evolution and hints at the wide dynamic range that a single fitness-enhancing mutation may display. It demonstrates how the modulation of a pleiotropic regulator can be used by cells to improve one trait while simultaneously work around other limiting constraints, by fine-tuning the expression of a wide range of cellular processes.

Electronic supplementary material

The online version of this article (10.1186/s12862-018-1331-x) contains supplementary material, which is available to authorized users.

Regulation of glycolytic flux and overflow metabolism depending on the source of energy generation for energy demand

Overflow metabolism is a common phenomenon observed at higher glycolytic flux in many bacteria, yeast (known as Crabtree effect), and mammalian cells including cancer cells (known as Warburg effect). This phenomenon has recently been characterized as the trade-offs between protein costs and enzyme efficiencies based on coarse-graining approaches. Moreover, it has been recognized that the glycolytic flux increases as the source of energy generation changes from energetically efficient respiration to inefficient respiro-fermentative or fermentative metabolism causing overflow metabolism. It is highly desired to clarify the metabolic regulation mechanisms behind such phenomena. Metabolic fluxes are located on top of the hierarchical regulation systems, and represent the outcome of the integrated response of all levels of cellular regulation systems. In the present article, we discuss about the different levels of regulation systems for the modulation of fluxes depending on the growth rate, growth condition such as oxygen limitation that alters the metabolism towards fermentation, and genetic perturbation affecting the source of energy generation from respiration to respiro-fermentative metabolism in relation to overflow metabolism. The intracellular metabolite of the upper glycolysis such as fructose 1,6-bisphosphate (FBP) plays an important role not only for flux sensing, but also for the regulation of the respiratory activity either directly or indirectly (via transcription factors) at higher growth rate. The glycolytic flux regulation is backed up (enhanced) by unphosphorylated EIIA and HPr of the phosphotransferase system (PTS) components, together with the sugar-phosphate stress regulation, where the transcriptional regulation is further modulated by post-transcriptional regulation via the degradation of mRNA (stability of mRNA) in Escherichia coli. Moreover, the channeling may also play some role in modulating the glycolytic cascade reactions.

The Evolution of gene regulation research in Lactococcus lactis

Lactococcus lactis is a major microbe. This lactic acid bacterium (LAB) is used worldwide in the production of safe, healthy, tasteful and nutritious milk fermentation products. Its huge industrial importance has led to an explosion of research on the organism, particularly since the early 1970s. The upsurge in the research on L. lactis coincided not accidentally with the advent of recombinant DNA technology in these years. The development of methods to take out and re-introduce DNA in L. lactis, to clone genes and to mutate the chromosome in a targeted way, to control (over)expression of proteins and, ultimately, the availability of the nucleotide sequence of its genome and the use of that information in transcriptomics and proteomics research have enabled to peek deep into the functioning of the organism. Among many other things, this has provided an unprecedented view of the major gene regulatory pathways involved in nitrogen and carbon metabolism and their overlap, and has led to the blossoming of the field of L. lactis systems biology. All of these advances have made L. lactis the paradigm of the LAB. This review will deal with the exciting path along which the research on the genetics of and gene regulation in L. lactis has trodden.

Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross-feeding microbial consortia

Resource scarcity is a common stress in nature and has a major impact on microbial physiology. This review highlights microbial acclimations to resource scarcity, focusing on resource investment strategies for chemoheterotrophs from the molecular level to the pathway level. Competitive resource allocation strategies often lead to a phenotype known as overflow metabolism; the resulting overflow byproducts can stabilize cooperative interactions in microbial communities and can lead to cross-feeding consortia. These consortia can exhibit emergent properties such as enhanced resource usage and biomass productivity. The literature distilled here draws parallels between in silico and laboratory studies and ties them together with ecological theories to better understand microbial stress responses and mutualistic consortia functioning.

Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield

Microbes may maximize the number of daughter cells per time or per amount of nutrients consumed. These two strategies correspond, respectively, to the use of enzyme-efficient or substrate-efficient metabolic pathways. In reality, fast growth is often associated with wasteful, yield-inefficient metabolism, and a general thermodynamic trade-off between growth rate and biomass yield has been proposed to explain this. We studied growth rate/yield trade-offs by using a novel modeling framework, Enzyme-Flux Cost Minimization (EFCM) and by assuming that the growth rate depends directly on the enzyme investment per rate of biomass production. In a comprehensive mathematical model of core metabolism in E. coli, we screened all elementary flux modes leading to cell synthesis, characterized them by the growth rates and yields they provide, and studied the shape of the resulting rate/yield Pareto front. By varying the model parameters, we found that the rate/yield trade-off is not universal, but depends on metabolic kinetics and environmental conditions. A prominent trade-off emerges under oxygen-limited growth, where yield-inefficient pathways support a 2-to-3 times higher growth rate than yield-efficient pathways. EFCM can be widely used to predict optimal metabolic states and growth rates under varying nutrient levels, perturbations of enzyme parameters, and single or multiple gene knockouts.

Systems biology of lactic acid bacteria: For food and thought

Lactic acid bacteria (LAB) ferment plants, fish, meats and milk and turn them into tasty food products with increased shelf life; other LAB help digesting food and create a healthy environment in the intestine. The economic and societal importance of these relatively simple and small bacteria is immense. In this review we hope to show that their adaptations to nutrient-rich environments provides fascinating and often puzzling behaviours that give rise to many fundamental evolutionary biological questions in need of a systems biology approach. We will provide examples of such questions, compare the (metabolic) behaviour of LAB to that of other model organisms, and provide the latest insights, if available.

Effects of glucose availability in Lactobacillus sakei; metabolic change and regulation of the proteome and transcriptome

Effects of glucose availability were investigated in Lactobacillus sakei strains 23K and LS25 cultivated in anaerobic, glucose-limited chemostats set at high (D = 0.357 h-1) and low (D = 0.045 h-1) dilution rates. We observed for both strains a shift from homolactic towards more mixed acid fermentation when comparing high to low growth rates. However, this change was more pronounced for LS25 than for 23K, where dominating products were lactate>formate>acetate≥ethanol at both conditions. A multivariate approach was used for analyzing proteome and transcriptome data from the bacterial cultures, where the predictive power of the omics data was used for identifying features that can explain the differences in the end-product profiles. We show that the different degree of response to the same energy restriction revealed interesting strain specific regulation. An elevated formate production level during slow growth, more for LS25 than for 23K, was clearly reflected in correlating pyruvate formate lyase expression. With stronger effect for LS25, differential expression of the Rex transcriptional regulator and NADH oxidase, a target of Rex, indicated that maintainance of the cell redox balance, in terms of the NADH/NAD+ ratio, may be a key process during the metabolic change. The results provide a better understanding of different strategies that cells may deploy in response to changes in substrate availability.

Microenvironmental cooperation promotes early spread and bistability of a Warburg-like phenotype

We introduce an in silico model for the initial spread of an aberrant phenotype with Warburg-like overflow metabolism within a healthy homeostatic tissue in contact with a nutrient reservoir (the blood), aimed at characterizing the role of the microenvironment for aberrant growth. Accounting for cellular metabolic activity, competition for nutrients, spatial diffusion and their feedbacks on aberrant replication and death rates, we obtain a phase portrait where distinct asymptotic whole-tissue states are found upon varying the tissue-blood turnover rate and the level of blood-borne primary nutrient. Over a broad range of parameters, the spreading dynamics is bistable as random fluctuations can impact the final state of the tissue. Such a behaviour turns out to be linked to the re-cycling of overflow products by non-aberrant cells. Quantitative insight on the overall emerging picture is provided by a spatially homogeneous version of the model.

The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization

Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as k_cat and K_M constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.

“Enzyme cost”, the amount of protein needed for a given metabolic flux, is crucial for the metabolic choices cells have to make. However, due to the technical limitations of linear optimization methods, this cost has traditionally been ignored by constraint-based metabolic models such as Flux Balance Analysis. On the other hand, more detailed kinetic models which use ordinary differential equations to simulate fluxes for different choices of enzyme allocation, are computationally demanding and not scalable enough. In this work, we developed a method which utilizes the full kinetic model to predict steady-state enzyme costs, using a scalable and robust algorithm based on convex optimization. We show that the minimization of enzyme cost is a meaningful optimality principle by comparing our predictions to measured enzyme and metabolite levels in exponentially growing E. coli. This method could be used to quantify the enzyme cost of many other pathways and explain why evolution has selected some low-yield metabolic strategies, including aerobic fermentation in yeast and cancer cells. Furthermore, future metabolic engineering projects could benefit from our method by choosing pathways that reduce the total amount of enzyme required for the synthesis of a value-added product.

High Dietary Selenium Intake Alters Lipid Metabolism and Protein Synthesis in Liver and Muscle of Pigs

BACKGROUND

Prolonged high intakes of dietary selenium have been shown to induce gestational diabetes in rats and hyperinsulinemia in pigs.

OBJECTIVE

Two experiments were conducted to explore metabolic and molecular mechanisms for the diabetogenic potential of high dietary selenium intakes in pigs.

METHODS

In Expt. 1, 16 Yorkshire-Landrace-Hampshire crossbred pigs (3 wk old, body weight = 7.5 ± 0.81 kg, 50% males and 50% females) were fed a corn-soybean meal basal diet supplemented with 0.3 or 1.0 mg Se/kg (as selenium-enriched yeast for 6 wk). In Expt. 2, 12 pigs of the same crossbreed (6 wk old, body weight = 16.0 ± 1.8 kg) were fed a similar basal diet supplemented with 0.3 or 3.0 mg Se/kg for 11 wk. Biochemical and gene and protein expression profiles of lipid and protein metabolism and selenoproteins in plasma, liver, muscle, and adipose tissues were analyzed.

RESULTS

In Expt. 1, the 1-mg-Se/kg diet did not affect body weight or plasma concentrations of glucose and nonesterified fatty acids. In Expt. 2, the 3-mg-Se/kg diet, compared with the 0.3-mg-Se/kg diet, increased (P < 0.05) concentrations of plasma insulin (0.2 compared with 0.4 ng/mL), liver and adipose lipids (41% to 2.4-fold), and liver and muscle protein (10-14%). In liver, the 3-mg-Se/kg diet upregulated (P < 0.05) the expression, activity, or both of key factors related to gluconeogenesis [phosphoenolpyruvate carboxykinase (PEPCK); 13%], lipogenesis [sterol regulatory element binding protein 1 (SREBP1), acetyl-coenzyme A carboxylase (ACC), and fatty acid synthase (FASN); 46-90%], protein synthesis [insulin receptor (INSR), P70 ribosomal protein S6 kinase (P70), and phosphorylated ribosomal protein S6 (P-S6); 88-105%], energy metabolism [AMP-activated protein kinase (AMPK); up to 2.8-fold], and selenoprotein glutathione peroxidase 3 (GPX3; 1.4-fold) and suppressed (P < 0.05) mRNA levels of lipolysis gene cytochrome P450, family 7, subfamily A, polypeptide 1 (CYP7A1; 88%) and selenoprotein gene selenoprotein W1 (SEPW1; 46%). In muscle, the 3-mg-Se/kg diet exerted no effect on the lipid profiles but enhanced (P < 0.05) expression of P-S6 and mammalian target of rapamycin (mTOR; 42-176%; protein synthesis); selenoprotein P (SELP; 40-fold); and tumor suppressor protein 53 (P53) and peroxisome proliferator-activated receptor γ (PPARG; 52-58%; lipogenesis) and suppressed (P < 0.05) expression of INSR (59%; insulin signaling); selenoprotein S (SELS); deiodinases, iodothyronine, type I (DIO1); and thioredoxin reductase 1 (TXNRD1; 50%; selenoproteins); and ACC1 and FASN (35-51%; lipogenesis).

CONCLUSION

Our research showed novel roles, to our best knowledge, and mechanisms of high selenium intakes in regulating the metabolism of protein, along with that of lipid, in a tissue-specific fashion in pigs.

Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments

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.

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.

Amino acid analysis using chromatography-mass spectrometry: An inter platform comparison study

The analysis of amino acids has become a central task in many aspects. While amino acid analysis has traditionally mainly been carried out using either gas chromatography (GC) in combination with flame ionization detection or liquid chromatography (LC) with either post-column derivatization using ninhydrin or pre-column derivatization using o-phthalaldehyde, many of today's analysis platforms are based on chromatography in combination with mass spectrometry (MS). While derivatization is mandatory for the GC-based analysis of amino acids, several LC platforms have emerged, particularly in the dawn of targeted metabolite profiling using hydrophilic interaction liquid chromatography (HILIC) coupled to MS, allowing the analysis of underivatized amino acids. Among the numerous analytical platforms available for amino acid analysis today, we here compare three prominent approaches, being GC-MS and LC-MS after amino acid derivatization using chloroformate and HILIC-MS of underivatized amino acids. We compare and discuss practical issues as well as performance characteristics, e.g., the use of (13)C-labeled internal standards, of the different platforms and present data on their practical implementation in our laboratory. Finally, we compare the real-life applicability of all three platforms for a complex biological sample. While all three platforms are very-well suited for the analysis of complex biological samples they all show advantages and disadvantages for some analytes as discussed in detail in this manuscript.