The purpose and ubiquity of turnover

Turnover-constant component production and destruction-is ubiquitous in biology. Turnover occurs across organisms and scales, including for RNAs, proteins, membranes, macromolecular structures, organelles, cells, hair, feathers, nails, antlers, and teeth. For many systems, turnover might seem wasteful when degraded components are often fully functional. Some components turn over with shockingly high rates and others do not turn over at all, further making this process enigmatic. However, turnover can address fundamental problems by yielding powerful properties, including regeneration, rapid repair onset, clearance of unpredictable damage and errors, maintenance of low constitutive levels of disrepair, prevention of stable hazards, and transitions. I argue that trade-offs between turnover benefits and metabolic costs, combined with constraints on turnover, determine its presence and rates across distinct contexts. I suggest that the limits of turnover help explain aging and that turnover properties and the basis for its levels underlie this fundamental component of life.

Comprehensive quantitative modeling of translation efficiency in a genome-reduced bacterium

Translation efficiency has been mainly studied by ribosome profiling, which only provides an incomplete picture of translation kinetics. Here, we integrated the absolute quantifications of tRNAs, mRNAs, RNA half-lives, proteins, and protein half-lives with ribosome densities and derived the initiation and elongation rates for 475 genes (67% of all genes), 73 with high precision, in the bacterium Mycoplasma pneumoniae (Mpn). We found that, although the initiation rate varied over 160-fold among genes, most of the known factors had little impact on translation efficiency. Local codon elongation rates could not be fully explained by the adaptation to tRNA abundances, which varied over 100-fold among tRNA isoacceptors. We provide a comprehensive quantitative view of translation efficiency, which suggests the existence of unidentified mechanisms of translational regulation in Mpn.

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (k_app) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.

Protein degradation sets the fraction of active ribosomes at vanishing growth

Growing cells adopt common basic strategies to achieve optimal resource allocation under limited resource availability. Our current understanding of such “growth laws” neglects degradation, assuming that it occurs slowly compared to the cell cycle duration. Here we argue that this assumption cannot hold at slow growth, leading to important consequences. We propose a simple framework showing that at slow growth protein degradation is balanced by a fraction of “maintenance” ribosomes. Consequently, active ribosomes do not drop to zero at vanishing growth, but as growth rate diminishes, an increasing fraction of active ribosomes performs maintenance. Through a detailed analysis of compiled data, we show that the predictions of this model agree with data from E. coli and S. cerevisiae. Intriguingly, we also find that protein degradation increases at slow growth, which we interpret as a consequence of active waste management and/or recycling. Our results highlight protein turnover as an underrated factor for our understanding of growth laws across kingdoms.

The idea that simple quantitative relationships relate cell physiology to cellular composition dates back to the 1950s, but the recent years saw a leap in our understanding of such “growth laws”, with relevant implications regarding the interdependence between growth, metabolism and biochemical networks. However, recent works on nutrient-limited growth mainly focused on laboratory conditions that are favourable to growth. Thus, our current mathematical understanding of the growth laws neglects protein degradation, under the argument that it occurs slowly compared to the timescale of the cell cycle. Instead, at slow growth the timescales of mass loss from protein degradation and dilution become comparable. In this work, we propose that protein degradation shapes the quantitative relationships between ribosome allocation and growth rate, and determines a fraction of ribosomes that do not contribute to growth and need to remain active to balance degradation.

Requirement of de novo synthesis of pyruvate carboxylase in long-term succinic acid production in Corynebacterium glutamicum

Protein turnover through de novo synthesis is critical for sustainable cellular functions. We previously found that glucose consumption rate in Corynebacterium glutamicum under anaerobic conditions increased at temperature higher than the upper limit of growth temperature. Here, we showed that production of lactic and succinic acids increased at higher temperature for long-term (48 h) anaerobic reaction in metabolically engineered strains. At 42 °C, beyond the upper limit of growth temperature range, biomass-specific lactic acid production rate was 8% higher than that at 30 °C, the optimal growth temperature. In contrast, biomass-specific succinic acid production rate was highest at 36 °C, 28% higher than that at 30 °C, although the production at 42 °C was still 23% higher than that at 30 °C. As enzymes are usually unstable at high temperatures, we investigated whether protein turnover of metabolic enzymes is required for the production of lactic and succinic acids under these conditions. Interestingly, when de novo protein synthesis was inhibited by addition of chloramphenicol, after 6 h, only succinic acid production was inhibited. Because glycolytic enzymes are involved in both lactic and succinic acids synthesis, enzymes in the anaplerotic pathway and the tricarboxylic acid (TCA) cycle leading to succinic acid synthesis were likely to be responsible for its decreased production. Among the five enzymes examined, the specific activity of only pyruvate carboxylase was drastically decreased after 48 h at 42 °C. Thus, the de novo synthesis of pyruvate carboxylase is required for long-term production of succinic acid. Graphical abstract KEY POINTS: • Long-term reaction for organic acids can be improved at temperature beyond ideal growth conditions. • De novo synthesis of pyruvate carboxylase is required for long-term succinic acid production.

Diverse conditions support near-zero growth in yeast: Implications for the study of cell lifespan

Baker's yeast has a finite lifespan and ages in two ways: a mother cell can only divide so many times (its replicative lifespan), and a non-dividing cell can only live so long (its chronological lifespan). Wild and laboratory yeast strains exhibit natural variation for each type of lifespan, and the genetic basis for this variation has been generalized to other eukaryotes, including metazoans. To date, yeast chronological lifespan has chiefly been studied in relation to the rate and mode of functional decline among non-dividing cells in nutrient-depleted batch culture. However, this culture method does not accurately capture two major classes of long-lived metazoan cells: cells that are terminally differentiated and metabolically active for periods that approximate animal lifespan (e.g. cardiac myocytes), and cells that are pluripotent and metabolically quiescent (e.g. stem cells). Here, we consider alternative ways of cultivating Saccharomyces cerevisiae so that these different metabolic states can be explored in non-dividing cells: (i) yeast cultured as giant colonies on semi-solid agar, (ii) yeast cultured in retentostats and provided sufficient nutrients to meet minimal energy requirements, and (iii) yeast encapsulated in a semisolid matrix and fed ad libitum in bioreactors. We review the physiology of yeast cultured under each of these conditions, and explore their potential to provide unique insights into determinants of chronological lifespan in the cells of higher eukaryotes.

The substrate specificity of eukaryotic cytosolic chaperonin CCT

The cytosolic chaperonin CCT (chaperonin containing TCP-1) is an ATP-dependent double-ring protein machine mediating the folding of members of the eukaryotic cytoskeletal protein families. The actins and tubulins are obligate substrates of CCT because they are completely dependent on CCT activity to reach their native states. Genetic and proteomic analysis of the CCT interactome in the yeast Saccharomyces cerevisiae revealed a CCT network of approximately 300 genes and proteins involved in many fundamental biological processes. We classified network members into sets such as substrates, CCT cofactors and CCT-mediated assembly processes. Many members of the 7-bladed propeller family of proteins are commonly found tightly bound to CCT isolated from human and plant cells and yeasts. The anaphase promoting complex (APC/C) cofactor propellers, Cdh1p and Cdc20p, are also obligate substrates since they both require CCT for folding and functional activation. In vitro translation analysis in prokaryotic and eukaryotic cell extracts of a set of yeast propellers demonstrates their highly differential interactions with CCT and GroEL (another chaperonin). Individual propeller proteins have idiosyncratic interaction modes with CCT because they emerged independently with neo-functions many times throughout eukaryotic evolution. We present a toy model in which cytoskeletal protein biogenesis and folding flux through CCT couples cell growth and size control to time dependent cell cycle mechanisms.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Determinants and Regulation of Protein Turnover in Yeast

Protein turnover maintains the recycling needs of the proteome, and its malfunction has been linked to aging and age-related diseases. However, not all proteins turnover equally, and the factors that contribute to accelerate or slow down turnover are mostly unknown. We measured turnover rates for 3,160 proteins in exponentially growing yeast and analyzed their dependence on physical, functional, and genetic properties. We found that functional characteristics, including protein localization, complex membership, and connectivity, have greater effect on turnover than sequence elements. We also found that protein turnover and mRNA turnover are correlated. Analysis under nutrient perturbation and osmotic stress revealed that protein turnover highly depends on cellular state and is faster when proteins are being actively used. Finally, stress-induced changes in protein and transcript abundance correlated with changes in protein turnover. This study provides a resource of protein turnover rates and principles to understand the recycling needs of the proteome under basal conditions and perturbation.

A Simple Light Isotope Metabolic Labeling (SLIM-labeling) Strategy: A Powerful Tool to Address the Dynamics of Proteome Variations In Vivo

Many quantitative proteomics strategies rely on in vivo metabolic incorporation of amino acids with modified stable isotope profiles into proteins. These methods give rise to multiple ions for each peptide, with possible distortion of the isotopolog distribution, making the overall analytical process complex. We validated an alternative strategy, simple light isotope metabolic labeling (SLIM-labeling), which alleviates many of these problems. SLIM-labeling is based on the in vivo reduction of the isotopic composition of proteins using metabolic precursors with a unique light isotope composition to label all amino acids. This brings a new dimension to in-depth, high resolution MS-based quantitative proteomics. Here, we describe a ¹²C-based SLIM-labeling strategy using U-[¹²C]-glucose as the metabolic precursor of all amino acids in the pathogenic yeast Candida albicans Monoisotopic ion intensity increased exponentially following ¹²C enrichment, substantially improving peptide identification scores and protein sequence coverage in bottom-up analyses. Multiplexing samples of ¹²C composition varying from natural abundance (98.93%) to 100% makes it possible to address relative quantification issues, keeping all the critical information for each peptide within a single isotopolog cluster. We applied this method to measure, for the first time, protein turnover at the proteome scale in Candida albicans and its modulation by inhibitors of the proteasome and vacuolar protein degradation systems.

Sequence-based analysis of protein degradation rates

Protein turnover is a key aspect of cellular homeostasis and proteome dynamics. However, there is little consensus on which properties of a protein determine its lifetime in the cell. In this work, we exploit two reliable datasets of experimental protein degradation rates to learn models and uncover determinants of protein degradation, with particular focus on properties that can be derived from the sequence. Our work shows that simple sequence features suffice to obtain predictive models of which the output correlates reasonably well with the experimentally measured values. We also show that intrinsic disorder may have a larger effect than previously reported, and that the effect of PEST regions, long thought to act as specific degradation signals, can be better explained by their disorder. We also find that determinants of protein degradation depend on the cell types or experimental conditions studied. This analysis serves as a first step towards the development of more complex, mature computational models of degradation of proteins and eventually of their full life cycle. Proteins 2017; 85:1593-1601. © 2017 Wiley Periodicals, Inc.

Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast

Protein synthesis is the most energy-consuming process in a proliferating cell, and understanding what controls protein abundances represents a key question in biology and biotechnology. We quantified absolute abundances of 5,354 mRNAs and 2,198 proteins in Saccharomyces cerevisiae under ten environmental conditions and protein turnover for 1,384 proteins under a reference condition. The overall correlation between mRNA and protein abundances across all conditions was low (0.46), but for differentially expressed proteins (n = 202), the median mRNA-protein correlation was 0.88. We used these data to model translation efficiencies and found that they vary more than 400-fold between genes. Non-linear regression analysis detected that mRNA abundance and translation elongation were the dominant factors controlling protein synthesis, explaining 61% and 15% of its variance. Metabolic flux balance analysis further showed that only mitochondrial fluxes were positively associated with changes at the transcript level. The present dataset represents a crucial expansion to the current resources for future studies on yeast physiology.

Protein turnover measurement using selected reaction monitoring-mass spectrometry (SRM-MS)

Protein turnover represents an important mechanism in the functioning of cells, with deregulated synthesis and degradation of proteins implicated in many diseased states. Therefore, proteomics strategies to measure turnover rates with high confidence are of vital importance to understanding many biological processes. In this study, the more widely used approach of non-targeted precursor ion signal intensity (MS1) quantification is compared with selected reaction monitoring (SRM), a data acquisition strategy that records data for specific peptides, to determine if improved quantitative data would be obtained using a targeted quantification approach. Using mouse liver as a model system, turnover measurement of four tricarboxylic acid cycle proteins was performed using both MS1 and SRM quantification strategies. SRM outperformed MS1 in terms of sensitivity and selectivity of measurement, allowing more confident determination of protein turnover rates. SRM data are acquired using cheaper and more widely available tandem quadrupole mass spectrometers, making the approach accessible to a larger number of researchers than MS1 quantification, which is best performed on high mass resolution instruments. SRM acquisition is ideally suited to focused studies where the turnover of tens of proteins is measured, making it applicable in determining the dynamics of proteins complexes and complete metabolic pathways.This article is part of the themed issue 'Quantitative mass spectrometry'.

Five Questions (with their Answers) on ER-Associated Degradation

Production of a functional proteome is a major burden for our cells. Native proteins operate inside and outside the cells to eventually warrant life and adaptation to metabolic and environmental changes, there is no doubt that production and inappropriate handling of misfolded proteins may cause severe disease states. This review focuses on protein destruction, which is, paradoxically, a crucial event for cell and organism survival. It regulates the physiological turnover of proteins and the clearance of faulty biosynthetic products. It mainly relies on the intervention of two catabolic machineries, the ubiquitin proteasome system and the (auto)lysosomal system. Here, we have selected five questions dealing with how, why and when proteins produced in the mammalian endoplasmic reticulum are eventually selected for destruction.

Protein turnover analysis in Salmonella Typhimurium during infection by dynamic SILAC, Topograph, and quantitative proteomics

Protein turnover affects protein abundance and phenotypes. Comprehensive investigation of protein turnover dynamics has the potential to provide substantial information about gene expression. Here we report a large-scale protein turnover study in Salmonella Typhimurium during infection by quantitative proteomics. Murine macrophage-like RAW 264.7 cells were infected with SILAC labeled Salmonella. Bacterial cells were extracted after 0, 30, 60, 120, and 240 min. Mass spectrometry analyses yielded information about Salmonella protein turnover dynamics and a software program named Topograph was used for the calculation of protein half lives. The half lives of 311 proteins from intracellular Salmonella were obtained. For bacteria cultured in control medium (DMEM), the half lives for 870 proteins were obtained. The calculated median of protein half lives was 69.13 and 99.30 min for the infection group and the DMEM group, respectively, indicating an elevated protein turnover at the initial stage of infection. Gene ontology analyses revealed that a number of protein functional groups were significantly regulated by infection, including proteins involved in ribosome, periplasmic space, cellular amino acid metabolic process, ion binding, and catalytic activity. The half lives of proteins involved in purine metabolism pathway were found to be significantly shortened during infection.

Yeast Interspecies Comparative Proteomics Reveals Divergence in Expression Profiles and Provides Insights into Proteome Resource Allocation and Evolutionary Roles of Gene Duplication

Omics analysis is a versatile approach for understanding the conservation and diversity of molecular systems across multiple taxa. In this study, we compared the proteome expression profiles of four yeast species (Saccharomyces cerevisiae, Saccharomyces mikatae, Kluyveromyces waltii, and Kluyveromyces lactis) grown on glucose- or glycerol-containing media. Conserved expression changes across all species were observed only for a small proportion of all proteins differentially expressed between the two growth conditions. Two Kluyveromyces species, both of which exhibited a high growth rate on glycerol, a nonfermentative carbon source, showed distinct species-specific expression profiles. In K. waltii grown on glycerol, proteins involved in the glyoxylate cycle and gluconeogenesis were expressed in high abundance. In K. lactis grown on glycerol, the expression of glycolytic and ethanol metabolic enzymes was unexpectedly low, whereas proteins involved in cytoplasmic translation, including ribosomal proteins and elongation factors, were highly expressed. These marked differences in the types of predominantly expressed proteins suggest that K. lactis optimizes the balance of proteome resource allocation between metabolism and protein synthesis giving priority to cellular growth. In S. cerevisiae, about 450 duplicate gene pairs were retained after whole-genome duplication. Intriguingly, we found that in the case of duplicates with conserved sequences, the total abundance of proteins encoded by a duplicate pair in S. cerevisiae was similar to that of protein encoded by nonduplicated ortholog in Kluyveromyces yeast. Given the frequency of haploinsufficiency, this observation suggests that conserved duplicate genes, even though minor cases of retained duplicates, do not exhibit a dosage effect in yeast, except for ribosomal proteins. Thus, comparative proteomic analyses across multiple species may reveal not only species-specific characteristics of metabolic processes under nonoptimal culture conditions but also provide valuable insights into intriguing biological principles, including the balance of proteome resource allocation and the role of gene duplication in evolutionary history.

Phosphorylation of ubiquitin at Ser65 affects its polymerization, targets, and proteome-wide turnover

Ubiquitylation is an essential post-translational modification that regulates numerous cellular processes, most notably protein degradation. Ubiquitin can itself be phosphorylated at nearly every serine, threonine, and tyrosine residue. However, the effect of this modification on ubiquitin function is largely unknown. Here, we characterized the effects of phosphorylation of yeast ubiquitin at serine 65 in vivo and in vitro. We find this post-translational modification to be regulated under oxidative stress, occurring concomitantly with the restructuring of the ubiquitin landscape into a highly polymeric state. Phosphomimetic mutation of S65 recapitulates the oxidative stress phenotype, causing a dramatic accumulation of ubiquitylated proteins and a proteome-wide reduction of protein turnover rates. Importantly, this mutation impacts ubiquitin chain disassembly, chain linkage distribution, ubiquitin interactions, and substrate targeting. These results demonstrate that phosphorylation is an additional mode of ubiquitin regulation with broad implications in cellular physiology.

Protein degradation corrects for imbalanced subunit stoichiometry in OST complex assembly

A combination of SILAC and targeted mass spectrometry provides a sensitive method to measure protein half-lives in yeast. Degradation rates are generally low in wild-type cells; however, ERAD is important to correct for imbalanced subunit stoichiometry. This approach is used to establish an assembly model for the OST complex.

Protein degradation is essential for cellular homeostasis. We developed a sensitive approach to examining protein degradation rates in Saccharomyces cerevisiae by coupling a SILAC approach to selected reaction monitoring (SRM) mass spectrometry. Combined with genetic tools, this analysis made it possible to study the assembly of the oligosaccharyl transferase complex. The ER-associated degradation machinery compensated for disturbed homeostasis of complex components by degradation of subunits in excess. On a larger scale, protein degradation in the ER was found to be a minor factor in the regulation of protein homeostasis in exponentially growing cells, but ERAD became relevant when the gene dosage was affected, as demonstrated in heterozygous diploid cells. Hence the alleviation of fitness defects due to abnormal gene copy numbers might be an important function of protein degradation.

Quantitative isoform-profiling of highly diversified recognition molecules

Complex biological systems rely on cell surface cues that govern cellular self-recognition and selective interactions with appropriate partners. Molecular diversification of cell surface recognition molecules through DNA recombination and complex alternative splicing has emerged as an important principle for encoding such interactions. However, the lack of tools to specifically detect and quantify receptor protein isoforms is a major impediment to functional studies. We here developed a workflow for targeted mass spectrometry by selected reaction monitoring that permits quantitative assessment of highly diversified protein families. We apply this workflow to dissecting the molecular diversity of the neuronal neurexin receptors and uncover an alternative splicing-dependent recognition code for synaptic ligands.

DOI:http://dx.doi.org/10.7554/eLife.07794.001

To create a protein, a gene is first copied to form an RNA molecule that contains regions known as introns and exons. Splicing removes the introns and joins the exons together to form a molecule of ‘messenger RNA’, which is translated into a protein.

Over the course of evolution, many groups—or families—of proteins have expanded and diversified their roles. One way in which this can occur is through a process known as alternative splicing, in which different exons can be included or excluded to generate the final messenger RNA. In this way, a single gene can produce a number of different proteins. These closely related proteins are known as isoforms.

The brain contains billions of neurons that communicate with one another across connections known as synapses. A family of proteins called neurexins helps neurons to form these synapses. Humans have three neurexin genes, which undergo extensive alternative splicing to produce thousands of protein isoforms. However, it is not known whether all of these isoforms are produced in neurons, as existing experimental techniques were not sensitive enough to easily distinguish one isoform from another.

A technique known as ‘selected reaction monitoring’ (or SRM for short) has recently emerged as a promising way to identify proteins. This allows proteins containing specific sequences to be separated out for analysis, in contrast to existing techniques that test randomly selected protein samples, which will result in most isoforms being missed. Schreiner, Simicevic et al. have now developed SRM further and show that this technique can detect the identity and amount of the neurexin isoforms present at synapses, including those that are only produced in very small quantities. Using SRM, Schreiner, Simicevic et al. demonstrate that neurexin isoforms differ in how they interact with synaptic receptors. Thus, alternative splicing of neurexins underlies a ‘recognition code’ at neuronal synapses.

In the future, this newly developed SRM method could be used to investigate isoforms in other protein families and tissues, and so may prove valuable for understanding how a wide range of cellular recognition processes work.

DOI:http://dx.doi.org/10.7554/eLife.07794.002

Feasibility of protein turnover studies in prototroph Saccharomyces cerevisiae strains

Quantitative proteomics studies of yeast that use metabolic labeling with amino acids rely on auxotrophic mutations of one or more genes on the amino acid biosynthesis pathways. These mutations affect yeast metabolism and preclude the study of some biological processes. Overcoming this limitation, it has recently been described that proteins in a yeast prototrophic strain can also be metabolically labeled with heavy amino acids. However, the temporal profiles of label incorporation under the different phases of the prototroph's growth have not been examined. Labeling trajectories are important in the study of protein turnover and dynamics, in which label incorporation into proteins is monitored across many time points. Here we monitored protein labeling trajectories for 48 h after a pulse with heavy lysine in a yeast prototrophic strain and compared them with those of a lysine auxotrophic yeast. Labeling was successful in prototroph yeast during exponential growth phase but not in stationary phase. Furthermore, we were able to determine the half-lives of more than 1700 proteins during exponential phase of growth with high accuracy and reproducibility. We found a median half-life of 2 h in both strains, which corresponds with the cellular doubling time. Nucleolar and ribosomal proteins showed short half-lives, whereas mitochondrial proteins and other energy production enzymes presented longer half-lives. Except for some proteins involved in lysine biosynthesis, we observed a high correlation in protein half-lives between prototroph and auxotroph strains. Overall, our results demonstrate the feasibility of using prototrophs for proteomic turnover studies and provide a reliable data set of protein half-lives in exponentially growing yeast.

Global proteome turnover analyses of the Yeasts S. cerevisiae and S. pombe

How cells maintain specific levels of each protein and whether that control is evolutionarily conserved are key questions. Here, we report proteome-wide steady-state protein turnover rate measurements for the evolutionarily distant but ecologically similar yeasts, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We find that the half-life of most proteins is much longer than currently thought and determined to a large degree by protein synthesis and dilution due to cell division. However, we detect a significant subset of proteins (∼15%) in both yeasts that are turned over rapidly. In addition, the relative abundances of orthologous proteins between the two yeasts are highly conserved across the 400 million years of evolution. In contrast, their respective turnover rates differ considerably. Our data provide a high-confidence resource for studying protein degradation in common yeast model systems.

Proteome adaptation of Saccharomyces cerevisiae to severe calorie restriction in Retentostat cultures

Stationary-phase, carbon-starved shake-flask cultures of Saccharomyces cerevisiae are popular models for studying eukaryotic chronological aging. However, their nutrient-starved physiological status differs substantially from that of postmitotic metazoan cells. Retentostat cultures offer an attractive alternative model system in which yeast cells, maintained under continuous calorie restriction, hardly divide but retain high metabolic activity and viability for prolonged periods of time. Using TMT labeling and UHPLC-MS/MS, the present study explores the proteome of yeast cultures during transition from exponential growth to near-zero growth in severely calorie-restricted retentostats. This transition elicited protein level changes in 20% of the yeast proteome. Increased abundance of heat shock-related proteins correlated with increased transcript levels of the corresponding genes and was consistent with a strongly increased heat shock tolerance of retentostat-grown cells. A sizable fraction (43%) of the proteins with increased abundance under calorie restriction was involved in oxidative phosphorylation and in various mitochondrial functions that, under the anaerobic, nongrowing conditions used, have a very limited role. Although it may seem surprising that yeast cells confronted with severe calorie restriction invest in the synthesis of proteins that, under those conditions, do not contribute to fitness, these responses may confer metabolic flexibility and thereby a selective advantage in fluctuating natural habitats.

Protein turnover forms one of the highest maintenance costs in Lactococcus lactis

Protein turnover plays an important role in cell metabolism by regulating metabolic fluxes. Furthermore, the energy costs for protein turnover have been estimated to account for up to a third of the total energy production during cell replication and hence may represent a major limiting factor in achieving either higher biomass or production yields. This work aimed to measure the specific growth rate (μ)-dependent abundance and turnover rate of individual proteins, estimate the ATP cost for protein production and turnover, and compare this with the total energy balance and other maintenance costs. The lactic acid bacteria model organism Lactococcus lactis was used to measure protein turnover rates at μ = 0.1 and 0.5 h(-1) in chemostat experiments. Individual turnover rates were measured for ~75% of the total proteome. On average, protein turnover increased by sevenfold with a fivefold increase in growth rate, whilst biomass yield increased by 35%. The median turnover rates found were higher than the specific growth rate of the bacterium, which suggests relatively high energy consumption for protein turnover. We found that protein turnover costs alone account for 38 and 47% of the total energy produced at μ = 0.1 and 0.5 h(-1), respectively, and gene ontology groups Energy metabolism and Translation dominated synthesis costs at both growth rates studied. These results reflect the complexity of metabolic changes that occur in response to changes in environmental conditions, and signify the trade-off between biomass yield and the need to produce ATP for maintenance processes.

Proteome dynamics: revisiting turnover with a global perspective

Although bulk protein turnover has been measured with the use of stable isotope labeled tracers for over half a century, it is only recently that the same approach has become applicable to the level of the proteome, permitting analysis of the turnover of many proteins instead of single proteins or an aggregated protein pool. The optimal experimental design for turnover studies is dependent on the nature of the biological system under study, which dictates the choice of precursor label, protein pool sampling strategy, and treatment of data. In this review we discuss different approaches and, in particular, explore how complexity in experimental design and data processing increases as we shift from unicellular to multicellular systems, in particular animals.